Machine Learning Resources

This is a curated list of resources that I have found useful regarding machine learning, in particular deep learning. Only books that add significant value to understanding the topic are listed. Also, a list of good articles and some other resources.

Background

• Introduction to Mathematical Statistics
  Robert Hogg, Joeseph McKean and Allen Craig

• Numerical Linear Algebra and Applications
  Biswa Nath Datta

• Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares

• Linear Algebra

Introductory

• A Brief Introduction to Machine Learning for Engineers

• Foundations of Machine Learning

• Introduction To Machine Learning
  Ethem Alpaydin

Advanced

• Reinforcement Learning: An Introduction
  Richard Sutton and Andrew Barto

• The Elements of Statistical Learning
  Trevor Hastie, Robert Tibshirani and Jerome Friedman

• Pattern Recognition and Machine Learning
  Christopher Bishop

• Machine Learning: A Probabilistic Perspective
  Kevin Murphy

Deep Learning Specific

• Deep Learning
  Ian Goodfellow, Yoshua Bengio and Aaron Courville

• Deep Learning with Python
  François Chollet

• Mathematics For Machine Learning

• A Gentle Introduction to Deep Learning for Graphs

• Dive into Deep Learning

Background Literature

• Intro to Neural Networks

• Dive into Deep Learning

• Probability Cheatsheet

• The Matrix Calculus You Need For Deep Learning

• Reinforcement Learning: Second Edition

• An Introduction to Probabilistic Programming

• Variational Inference: A Review for Statisticians

• A Tutorial on Energy-Based Learning

Interesting and Relevant Articles

• U2-ONet: A Two-level Nested Octave U-structure with Multiscale Attention Mechanism for Moving Instances Segmentation

• From proprioception to long-horizon planning in novel environments: A hierarchical RL model

• Quality Diversity Through Surprise

• Randomized Automatic Differentiation

• The Differentiable Cross-Entropy Method

• SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning

• SegFix: Model-Agnostic Boundary Refinement for Segmentation

• The Go Transformer: Natural Language Modeling for Game Play

• Object Goal Navigation using Goal-Oriented Semantic Exploration

• High-Fidelity Generative Image Compression

• Simulation-based Lidar Super-resolution for Ground Vehicles

• Designing Data Augmentation for Simulating Interventions

• RMPE: Regional Multi-Person Pose Estimation

• Bilateral Attention Network for RGB-D Salient Object Detection

• GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce

• ROAD SEGMENTATION ON LOW RESOLUTION LIDAR POINT CLOUDS FOR AUTONOMOUS VEHICLES

• Multimodal and multiview distillation for real-time player detection on a football field

• Deep High-Resolution Representation Learning for Human Pose Estimation

• 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds

• Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

• Deep White-Balance Editing

• Dynamic ReLU

• SEED RL: SCALABLE AND EFFICIENT DEEP-RL WITH ACCELERATED CENTRAL INFERENCE

• Redesigning SLAM for Arbitrary Multi-Camera Systems

• SOLOv2: Dynamic, Faster and Stronger

• LEARNING TO REACH GOALS WITHOUT REINFORCEMENT LEARNING

• Learning Predictive Representations for Deformable Objects Using Contrastive Estimation

• AutoML-Zero: Evolving Machine Learning Algorithms From Scratch

• TensorFlow Quantum: A Software Framework for Quantum Machine Learning

• LAGRANGIAN NEURAL NETWORKS

• SalsaNext: Fast Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving

• HeatNet: Bridging the Day-Night Domain Gap in Semantic Segmentation with Thermal Images

• MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships

• Wavesplit: End-to-End Speech Separation by Speaker Clustering

• Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

• BlenderProc

• RGPNET: A REAL-TIME GENERAL PURPOSE SEMANTIC SEGMENTATION

• Extended Isolation Forest

• Fast Differentiable Sorting and Ranking

• Bayesian Deep Learning and a Probabilistic Perspective of Generalization

• Wavesplit: End-to-End Speech Separation by Speaker Clustering

• Learning Interpretable Models in the Property Specification Language

• Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN

• RECURRENT INDEPENDENT MECHANISMS

• Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

• Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis

• The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence

• GEOM-GCN: GEOMETRIC GRAPH CONVOLUTIONAL NETWORKS

• BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

• Variational Autoencoders and Nonlinear ICA: A Unifying Framework

• Deep Reinforcement Learning for Autonomous Driving: A Survey

• SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

• Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

• ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes

• Fine-Tuning a Transformer-Based Language Model to Avoid Generating Non-Normative Text

• DeepErase: Weakly Supervised Ink Artifact Removal in Document Text Images

• SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation

• EdgeNets:Edge Varying Graph Neural Networks

• High Speed and High Dynamic Range Video with an Event Camera

• Pose-Assisted Multi-Camera Collaboration for Active Object Tracking

• MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection

• ON THE RELATIONSHIP BETWEEN SELF-ATTENTION AND CONVOLUTIONAL LAYERS

• STAViS: Spatio-Temporal AudioVisual Saliency Network

• OBJECTS DETECTION FOR REMOTE SENSING IMAGES BASED ON POLAR COORDINATES

• How neural networks find generalizable solutions: Self-tuned annealing in deep learning

• Make a Face: Towards Arbitrary High Fidelity Face Manipulation

• Latent ODEs for Irregularly-Sampled Time Series

• A Pre-defined Sparse Kernel Based Convolution for Deep CNNs

• Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

• FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System

• HAMILTONIAN GENERATIVE NETWORKS

• Robust Gabor Networks

• YOUR CLASSIFIER IS SECRETLY AN ENERGY BASED MODEL AND YOU SHOULD TREAT IT LIKE ONE

• Compositional generalization through meta sequence-to-sequence learning

• MixMatch: A Holistic Approach to Semi-Supervised Learning

• U-Net with spatial pyramid pooling for drusen segmentation in optical coherence tomography

• From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction

• Interrogating theoretical models of neural computation with deep inference

• PointRend: Image Segmentation as Rendering

• RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms

• Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks

• RED-NET: A RECURSIVE ENCODER-DECODER NETWORK FOR EDGE DETECTION

• Machine Unlearning

• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

• Putting An End to End-to-End: Gradient-Isolated Learning of Representations

• Geometric Capsule Autoencoders for 3D Point Clouds

• Training Agents using Upside-Down Reinforcement Learning

• DATA-EFFICIENT IMAGE RECOGNITION WITH CONTRASTIVE PREDICTIVE CODING

• Reinforcement Learning for Market Making in a Multi-agent Dealer Market

• A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

• Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data

• PolyTransform: Deep Polygon Transformer for Instance Segmentation

• Full-Gradient Representation for Neural Network Visualization

• The intriguing role of module criticality in the generalization of deep networks

• Automatic Video Object Segmentation via Motion-Appearance-Stream Fusion and Instance-aware Segmentation

• What’s Hidden in a Randomly Weighted Neural Network?

• ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation

• SuperGlue: Learning Feature Matching with Graph Neural Networks

• EfficientDet: Scalable and Efficient Object Detection

• Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing

• Gauge Equivariant Convolutional Networks and the Icosahedral CNN

• Continual Unsupervised Representation Learning

• Model Cards for Model Reporting

• Planning with Goal-Conditioned Policies

• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

• MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets

• Learning Autocomplete Systems as a Communication Game

• Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?

• ASV: Accelerated Stereo Vision System

• Robo-PlaNet: Learning to Poke in a Day

• Location-aware Upsampling for Semantic Segmentation

• Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors

• Learning From Brains How to Regularize Machines

• Learning from a Teacher using Unlabeled Data

• GLMNet: Graph Learning-Matching Networks for Feature Matching

• Machine learning for molecular simulation

• Multiple Futures Prediction

• Hyper-SAGNN: a self-attention based graph neural network for hypergraphs

• GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs

• Stacked Capsule Autoencoders

• Universal Intelligence: A Definition of Machine Intelligence

• Geometry-Aware Neural Rendering

• Momentum Contrast for Unsupervised Visual Representation Learning

• SO(8) Supergravity and the Magic of Machine Learning

• Reconciling modern machine learning practice and the bias-variance trade-off

• Optimizing Millions of Hyperparameters by Implicit Differentiation

• Shaping Visual Representations with Language for Few-shot Classification

• Experience-Embedded Visual Foresight

• Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

• Memory Augmented Recursive Neural Networks

• Self-training with Noisy Student improves ImageNet classification

• Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

• Story Realization: Expanding Plot Events into Sentences

• Nonlinearity + Networks: A 2020 Vision

• Worst Cases Policy Gradients

• Probabilistic Logic Neural Networks for Reasoning

• Improving Transformer Models by Reordering their Sublayers

• Practical Deep Learning with Bayesian Principles

• End to end learning and optimization on graphs

• Combinatorial Bayesian Optimization using the Graph Cartesian Product

• Dynamics-Aware Unsupervised Discovery of Skills

• The Measure of Intelligence

• The frontier of simulation-based inference

• Fast Transformer Decoding: One Write-Head is All You Need

• Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification

• CompILE: Compositional Imitation Learning and Execution

• Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

• Counterfactual Story Reasoning and Generation

• EDGEFOOL: AN ADVERSARIAL IMAGE ENHANCEMENT FILTER

• GENERALIZATION THROUGH MEMORIZATION: NEAREST NEIGHBOR LANGUAGE MODELS

• Integer Discrete Flows and Lossless Compression

• Generalization of Reinforcement Learners with Working and Episodic Memory

• CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data

• Learning to Fix Build Errors with Graph2Diff Neural Networks

• Image-Conditioned Graph Generation for Road Network Extraction

• Optuna: A Next-generation Hyperparameter Optimization Framework

• Learning Neural Networks with Adaptive Regularization

• Seeing What a GAN Cannot Generate

• Learning Transferable Graph Exploration

• On the Interaction Between Deep Detectors and Siamese Trackers in Video Surveillance

• Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

• Adversarial NLI: A New Benchmark for Natural Language Understanding

• Evaluating the Factual Consistency of Abstractive Text Summarization

• Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

• Learning Data Manipulation for Augmentation and Weighting

• Function-Space Distributions over Kernels

• Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning

• Neural Density Estimation and Likelihood-free Inference

• A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models

• Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

• SinGAN: Learning a Generative Model from a Single Natural Image

• Neural Network Distiller: A Python Package For DNN Compression Research

• Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

• DEEP CAUSAL REPRESENTATION LEARNING FOR UNSUPERVISED DOMAIN ADAPTATION

• EMERGENT TOOL USE FROM MULTI-AGENT AUTOCURRICULA

• Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

• Variational Temporal Abstraction

• DIFFTAICHI: DIFFERENTIABLE PROGRAMMING FOR PHYSICAL SIMULATION

• Capacity, Bandwidth, and Compositionality in Emergent Language Learning

• Few-Shot Unsupervised Image-to-Image Translation

• Cross-View Policy Learning for Street Navigation

• BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search

• High-Quality Self-Supervised Deep Image Denoising

• SCALE-EQUIVARIANT STEERABLE NETWORKS

• A General Framework for Uncertainty Estimation in Deep Learning

• AdaptIS: Adaptive Instance Selection Network

• Latent ODEs for Irregularly-Sampled Time Series

• WEAKLY SUPERVISED DISENTANGLEMENT WITH GUARANTEES

• ON MUTUAL INFORMATION MAXIMIZATION FOR REPRESENTATION LEARNING

• FIRST-ORDER PRECONDITIONING VIA HYPERGRADIENT DESCENT

• Deep Reinforcement Learning meets Graph Neural Networks: An optical network routing use case

• IMPROVING SAT SOLVER HEURISTICS WITH GRAPH NETWORKS AND REINFORCEMENT LEARNING

• Benchmarking Batch Deep Reinforcement Learning Algorithms

• LEARNING PARTIAL DIFFERENTIAL EQUATIONS FROM DATA USING NEURAL NETWORKS

• CASCADED GENERATION OF HIGH-QUALITY COLOR VISIBLE FACE IMAGES FROM THERMAL CAPTURES

• GPU-ACCELERATED VITERBI EXACT LATTICE DECODER FOR BATCHED ONLINE AND OFFLINE SPEECH RECOGNITION

• Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

• Gaze360: Physically Unconstrained Gaze Estimation in the Wild

• Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation

• Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin

• MultiFiT: Efficient Multi-lingual Language Model Fine-tuning

• Neural Logic Networks

• Precise measurement of quantum observables with neural-network estimators

• BIAS-RESILIENT NEURAL NETWORK

• Hair-GANs: Recovering 3D Hair Structure from a Single Image

• Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

• On the Utility of Learning about Humans for Human-AI Coordination

• Language Models as Knowledge Bases?

• Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer

• Restoring ancient text using deep learning: a case study on Greek epigraphy

• MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

• STABILIZING TRANSFORMERS FOR REINFORCEMENT LEARNING

• Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone Microcontroller

• QUANTIZED REINFORCEMENT LEARNING (QUARL)

• Landmarks-assisted Collaborative Deep Framework for Automatic 4D Facial Expression Recognition

• Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

• Emergent properties of the local geometry of neural loss landscapes

• Active Learning for Graph Neural Networks via Node Feature Propagation

• IMPLICIT COMPETITIVE REGULARIZATION IN GANS

• Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming

• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

• BOTORCH: PROGRAMMABLE BAYESIAN OPTIMIZATION IN PYTORCH

• Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs

• ON EMPIRICAL COMPARISONS OF OPTIMIZERS FOR DEEP LEARNING

• Optimal Training of Fair Predictive Models

• A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

• Exploring single-cell data with deep multitasking neural networks

• Mish: A Self Regularized Non-Monotonic Neural Activation Function

• Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning

• Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning

• Addressing Failure Prediction by Learning Model Confidence

• Bayesian Optimization Meets Riemannian Manifolds in Robot Learning

• FinBERT: Pre-Trained on SEC Filings for Financial NLP Tasks

• LEARN TO EXPLAIN EFFICIENTLY VIA NEURAL LOGIC INDUCTIVE LEARNING

• A Generalized Framework for Population Based Training

• DIVERSITY IS ALL YOU NEED: LEARNING SKILLS WITHOUT A REWARD FUNCTION

• BERTSCORE: EVALUATING TEXT GENERATION WITH BERT

• Fine-Grained Continual Learning

• Practical Posterior Error Bounds from Variational Objectives

• FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image

• Unsupervised Doodling and Painting with Improved SPIRAL

• Identifying Weights and Architectures of Unknown ReLU Networks

• LEARNING NEURAL CAUSAL MODELS FROM UNKNOWN INTERVENTIONS

• End-to-End Motion Planning of Quadrotors Using Deep Reinforcement Learning

• YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

• On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps

• Bootstrapping Conditional GANs for Video Game Level Generation

• CAUSAL INDUCTION FROM VISUAL OBSERVATIONS FOR GOAL DIRECTED TASKS

• Improving Map Re-localization with Deep ‘Movable’ Objects Segmentation on 3D LiDAR Point Clouds

• META-LEARNING DEEP ENERGY-BASED MEMORY MODELS

• Accelerating Federated Learning via Momentum Gradient Descent

• TorchBeast: A PyTorch Platform for Distributed RL

• SPECTRAL INFERENCE NETWORKS: UNIFYING DEEP AND SPECTRAL LEARNING

• Likelihood-free MCMC with Amortized Approximate Likelihood Ratios

• Learnable Triangulation of Human Pose

• DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare

• GRADIENT DESCENT: THE ULTIMATE OPTIMIZER

• COMPRESSIVE TRANSFORMERS FOR LONG-RANGE SEQUENCE MODELLING

• Regression Planning Networks

• Real-Time Semantic Stereo Matching

• Efficient Graph Generation with Graph Recurrent Attention Networks

• Equivariant Hamiltonian Flows

• Fermionic neural-network states for ab-initio electronic structure

• Machine learning for neural decoding

• Hamiltonian Graph Networks with ODE Integrators

• Mathematical Reasoning in Latent Space

• Hierarchical Decision Making by Generating and Following Natural Language Instructions

• Neural reparameterization improves structural optimization

• IS DEEP REINFORCEMENT LEARNING REALLY SUPER- HUMAN ON ATARI? LEVELING THE PLAYING FIELD

• BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks

• ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS

• Memory-Augmented Neural Networks for Machine Translation

• Deep Dynamics Models for Learning Dexterous Manipulation

• Quantum Graph Neural Networks

• RECURRENT INDEPENDENT MECHANISMS

• Mesh R-CNN

• Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

• Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz ’96 Model

• DyANE: Dynamics-aware node embedding for temporal networks

• Learning Priors for Adversarial Autoencoders

• ns3-gym: Extending OpenAI Gym for Networking Research

• A Probabilistic Representation of Deep Learning

• Making the Invisible Visible: Action Recognition Through Walls and Occlusions

• Exploring ways to incorporate additional knowledge to improve Natural Language Commonsense Question Answering

• Inception-inspired LSTM for Next-frame Video Prediction

• Discovering Reliable Correlations in Categorical Data

• Hierarchical Reinforcement Learning for Open-Domain Dialog

• Logarithmic Regret for Online Control

• Deep Multi-Agent Reinforcement Learning

• JOINT GRAPH AND FEATURE LEARNING IN GRAPH CONVOLUTIONAL NEURAL NETWORKS

• Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

• Meta-Curvature

• Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

• HoughNet: neural network architecture for vanishing points detection

• Implicit Autoencoders

• Reservoirs learn to learn

• Deep Boltzmann Machines

• The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

• What do Deep Networks Like to Read?

• Learning Symbolic Physics with Graph Networks

• HIERARCHICAL FORESIGHT: SELF-SUPERVISED LEARNING OF LONG-HORIZON TASKS VIA VISUAL SUBGOAL GENERATION

• Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking

• Memorize-Generalize: An online algorithm for learning higher-order sequential structure with cloned Hidden Markov Models

• CvxNets: Learnable Convex Decomposition

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• Video Interpolation and Prediction with Unsupervised Landmarks

• Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation

• CTRL: A CONDITIONAL TRANSFORMER LANGUAGE MODEL FOR CONTROLLABLE GENERATION

• On Extractive and Abstractive Neural Document Summarization with Transformer Language Models

• Meta-Learning with Implicit Gradients

• MODERN PERSPECTIVES ON REINFORCEMENT LEARNING IN FINANCE

• rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch

• Deep weakly-supervised learning methods for classification and localization in histology images: a survey

• Wave Physics as an Analog Recurrent Neural Network

• Universal Adversarial Triggers for Attacking and Analyzing NLP

• Deep Equilibrium Models

• ParaQG: A System for Generating Questions and Answers from Paragraphs

• Progressive Face Super-Resolution via Attention to Facial Landmark

• Misspelling Oblivious Word Embeddings

• Beyond Photo Realism for Domain Adaptation from Synthetic Data

• PaperRobot: Incremental Draft Generation of Scientific Ideas

• OpenSpiel: A Framework for Reinforcement Learning in Games

• Y-Autoencoders: disentangling latent representations via sequential-encoding

• Learning Speaker Representations with Mutual Information

• Hamiltonian Neural Networks

• Making Convolutional Networks Shift-Invariant Again

• A Baseline for 3D Multi-Object Tracking

• Behaviour Suite for Reinforcement Learning

• ON THE VARIANCE OF THE ADAPTIVE LEARNING RATE AND BEYOND

• MIDCURVENN: ENCODER-DECODER NEURAL NETWORK FOR COMPUTING MIDCURVE OF A THIN POLYGON

• Panoptic Feature Pyramid Networks

• Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System

• Tell Me Where to Look: Guided Attention Inference Network

• Latent ODEs for Irregularly-Sampled Time Series

• Adaptive Honeypot Engagement through Reinforcement Learning of Semi-Markov Decision Processes

• Sensor-Actuator Networks

• Anticipatory Thinking: A Metacognitive Capability

• Growing Action Spaces

• The Thermodynamic Variational Objective

• Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

• Adaptive Attention Span in Transformers

• YOLACT: Real-time Instance Segmentation

• Weight Agnostic Neural Networks

• Learning the Depths of Moving People by Watching Frozen People

• Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images

• A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

• Unsupervised Monocular Depth Estimation with Left-Right Consistency

• Self-supervised Learning for Video Correspondence Flow

• Early Action Prediction with Generative Adversarial Networks

• PR Product: A Substitute for Inner Product in Neural Networks

• Multi-level Encoder-Decoder Architectures for Image Restoration

• Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

• Multi-Sample Dropout for Accelerated Training and Better Generalization

• Pre-training Graph Neural Networks

• https://arxiv.org/pdf/1905.12107.pdf

• SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

• DECISION-BASED ADVERSARIAL ATTACKS: RELIABLE ATTACKS AGAINST BLACK-BOX MACHINE LEARNING MODELS

• 3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks

• Weight Agnostic Neural Networks

• Improving the Robustness of Deep Neural Networks via Stability Training

• Improved Precision and Recall Metric for Assessing Generative Models

• A Deep Journey into Super-resolution: A Survey

• SEARCHING FOR ACTIVATION FUNCTIONS

• Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

• Fooling automated surveillance cameras: adversarial patches to attack person detection

• Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras

• Inferring the quantum density matrix with machine learning

• WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation

• Robust Multi-agent Counterfactual Prediction

• Can WiFi Estimate Person Pose?

• Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce

• Exact Gaussian Processes on a Million Data Points

• TF-REPLICATOR: DISTRIBUTED MACHINE LEARNING FOR RESEARCHERS

• Bio-LSTM: A Biomechanically Inspired Recurrent Neural Network for 3D Pedestrian Pose and Gait Prediction

• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

• A Brief Survey of Deep Reinforcement Learning

• Topology of Learning in Artificial Neural Networks

• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

• Knowledge-based Analysis for Mortality Prediction from CT Images

• Language Models are Unsupervised Multitask Learners

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

• Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity

• Learning Unsupervised Learning Rules

• The Value Function Polytope in Reinforcement Learning

• Neural Inverse Knitting: From Images to Manufacturing Instructions

• A Geometric Perspective on Optimal Representations for Reinforcement Learning

• TF-REPLICATOR: DISTRIBUTED MACHINE LEARNING FOR RESEARCHERS

• VAE-GANs for Probabilistic Compressive Image Recovery: Uncertainty Analysis

• Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

• The Hanabi Challenge: A New Frontier for AI Research

• Towards reconstructing intelligible speech from the human auditory cortex

• Invertible Residual Networks

• Deep, Skinny Neural Networks are not Universal Approximators

• MONet: Unsupervised Scene Decomposition and Representation

• SelectiveNet: A Deep Neural Network with an Integrated Reject Option

• Theories of Error Back-Propagation in the Brain

• Adversarial Examples Target Topological Holes in Deep Networks

• Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning

• Redundant Perception and State Estimation for Reliable Autonomous Racing

• Guided Dropout

• On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

• Disentangling Disentanglement in Variational Auto-Encoders

• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

• Character-level Convolutional Networks for Text Classification

• Causal Reasoning from Meta-reinforcement Learning

• A Constructive Approach for One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings

• LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image

• Object Detection in Equirectangular Panorama

• Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

• A case for deep learning in semantics

• Bayesian Nonparametric Spectral Estimation

• Visualizing the Loss Landscape of Neural Nets

• HOUDINI: Lifelong Learning as Program Synthesis

• Global Pose Estimation with an Attention-based Recurrent Network

• ON THE TURING COMPLETENESS OF MODERN NEURAL NETWORK ARCHITECTURES

• Progressive Neural Architecture Search

• POINCARE ́ GLOVE: HYPERBOLIC WORD EMBEDDINGS

• THE DEEP WEIGHT PRIOR

• Generation Meets Recommendation: Proposing Novel Items for Groups of Users

• Deep Variational Reinforcement Learning for POMDPs

• Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

• Semi-supervised Tensor Factorization for Brain Network Analysis

• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

• Differentiable MPC for End-to-end Planning and Control

• Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning

• Image Segmentation with A Bounding Box Prior

• ClickBAIT-v2: Training an Object Detector in Real-Time

• OBJECT DETECTION BASED ON LIDAR TEMPORAL PULSES USING SPIKING NEURAL NETWORKS

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

• Trying to Understand Recurrent Neural Networks for Language Processing

• How deep is deep enough? - Optimizing deep neural network architecture

• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

• Deep Semantic Instance Segmentation of Tree-like Structures Using Synthetic Data

• Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation

• VIREL: A Variational Inference Framework for Reinforcement Learning

• Gradient Descent Finds Global Minima of Deep Neural Networks

• Zero-Shot Learning via Latent Space Encoding

• Inducing Interpretable Representations with Variational Autoencoders

• Glow: Generative Flow with Invertible 1×1 Convolutions

• Learning to Pivot with Adversarial Networks

• Residual Dense Network for Image Super-Resolution

• Deep Modeling Complex Couplings within Financial Markets

• TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

• SlowFast Networks for Video Recognition

• Counterfactuals uncover the modular structure of deep generative models

• Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder

• Random Erasing Data Augmentation

• From Recognition to Cognition: Visual Commonsense Reasoning

• On Challenges in Machine Learning Model Management

• Deep Learning with Mixed Supervision for Brain Tumor Segmentation

• Complex-YOLO: Real-time 3D Object Detection on Point Clouds

• A Probabilistic U-Net for Segmentation of Ambiguous Images

• A Theoretical Analysis of Deep Q-Learning

• A Tutorial on Deep Latent Variable Models of Natural Language

• Analogues of mental simulation and imagination in deep learning

• Neural Ordinary Differential Equations

• Gated-Dilated Networks for Lung Nodule Classification in CT scans

• Explanatory Graphs for CNNs

• Machine learning & artificial intelligence in the quantum domain

• GAN DISSECTION: VISUALIZING AND UNDERSTANDING GENERATIVE ADVERSARIAL NETWORKS

• Learning to Reason with Third-Order Tensor Products

• Playing hard exploration games by watching YouTube

• Learning to Infer Graphics Programs from Hand-Drawn Images

• LEARNING SPEAKER REPRESENTATIONS WITH MUTUAL INFORMATION

• How Does Batch Normalization Help Optimization?

• Residual Dense Network for Image Super-Resolution

• Predicting Opioid Relapse Using Social Media Data

• Non-Stationary Model for Crime Rate Inference Using Modern Urban Data

• Deep Cosine Metric Learning for Person Re-Identification

• Recurrent Relational Networks

• Macro action selection with deep reinforcement learning in StarCraft

• Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects

• Robust Artificial Intelligence and Robust Human Organizations

• Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network

• Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting

• Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration

• Matching Features without Descriptors: Implicitly Matched Interest Points (IMIPs)

• Partial Convolution based Padding

• THE VARIATIONAL FAIR AUTOENCODER

• Statistical physics of liquid brains

• Focal Loss for Dense Object Detection

• Interpretable Convolutional Filters with SincNet

• Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

• Human-like machine learning: limitations and suggestions

• WOULDA, COULDA, SHOULDA: COUNTERFACTUALLY-GUIDED POLICY SEARCH

• Learning Latent Dynamics for Planning from Pixels

• DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution

• Curiosity Driven Exploration of Learned Disentangled Goal Spaces

• CariGANs: Unpaired Photo-to-Caricature Translation

• Physical Signal Classification Via Deep Neural Networks

• Stochastic Deep Networks

• ADAPTIVE NEURAL TREES

• Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

• Predicting Diabetes Disease Evolution Using Financial Records and Recurrent Neural Networks

• Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

• Solving Imperfect-Information Games via Discounted Regret Minimization

• Stochastic Adaptive Neural Architecture Search for Keyword Spotting

• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

• A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

• FUSIONSTITCHING: DEEP FUSION AND CODE GENERATION FOR TENSORFLOW COMPUTATIONS ON GPUS

• ON THE STATISTICAL AND INFORMATION-THEORETIC CHARACTERISTICS OF DEEP NETWORK REPRESENTATIONS

• ImageNet/ResNet-50 Training in 224 Seconds

• A Convergence Theory for Deep Learning via Over-Parameterization

• Guided Dropout

• Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds

• Controllable Neural Story Generation via Reinforcement Learning

• Extending Pretrained Segmentation Networks with Additional Anatomical Structures

• The RLLChatbot: a solution to the ConvAI challenge

• Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training

• Activation Functions: Comparison of Trends in Practice and Research for Deep Learning

• MixTrain: Scalable Training of Formally Robust Neural Networks

• Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

• PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION

• Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms?

• FloWaveNet : A Generative Flow for Raw Audio

• Measuring the Effects of Data Parallelism on Neural Network Training

• Learning from Demonstration in the Wild

• Learning Distributed Representations of Symbolic Structure Using Binding and Unbinding Operations

• EXPLORATION BY RANDOM NETWORK DISTILLATION

• Towards a Simple Approach to Multi-step Model-based Reinforcement Learning

• HORIZON: FACEBOOK’S OPEN SOURCE APPLIED REINFORCEMENT LEARNING PLATFORM

• SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning

• CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

• Automatic Poetry Generation with Mutual Reinforcement Learning

• A General Method for Amortizing Variational Filtering

• Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs

• Reversible Recurrent Neural Networks

• One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

• Dendritic cortical microcircuits approximate the backpropagation algorithm

• Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search

• BABYAI: FIRST STEPS TOWARDS GROUNDED LAN- GUAGE LEARNING WITH A HUMAN IN THE LOOP

• Online Human Gesture Recognition using Recurrent Neural Networks and Wearable Sensors

• Deep Graph Convolutional Encoders for Structured Data to Text Generation

• Mask Propagation Network for Video Object Segmentation

• Unsupervised Statistical Machine Translation

• LEARNED OPTIMIZERS THAT OUTPERFORM SGD ON WALL-CLOCK AND VALIDATION LOSS

• Neighbourhood Consensus Networks

• Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

• Actor-Critic Policy Optimization in Partially Observable Multiagent Environments

• BrainNet: A Multi-Person Brain-to-Brain Interface for Direct Collaboration Between Brains

• Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

• DGC-Net: Dense Geometric Correspondence Network

• A Brief Survey of Deep Reinforcement Learning

• Trust Region Policy Optimization of POMDPs

• Gradient Agreement as an Optimization Objective for Meta-Learning

• Low-Shot Learning with Imprinted Weights

• Deep neural network based i-vector mapping for speaker verification using short utterances

• TRELLIS NETWORKS FOR SEQUENCE MODELING

• Variational Bayesian Monte Carlo

• Private Machine Learning in TensorFlow using Secure Computation

• Structural Sparsity of Complex Networks: Bounded Expansion in Random Models and Real-World Graphs

• UNSUPERVISED NEURAL MULTI-DOCUMENT ABSTRACTIVE SUMMARIZATION

• DISCRIMINATOR REJECTION SAMPLING

• At Human Speed: Deep Reinforcement Learning with Action Delay

• GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning

• CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning

• Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

• Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions

• Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

• 3D Hair Synthesis Using Volumetric Variational Autoencoders

• Recycle-GAN: Unsupervised Video Retargeting

• Self-Attention Generative Adversarial Networks

• Pruning neural networks: is it time to nip it in the bud?

• LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS

• Knowing Where to Look? Analysis on Attention of Visual Question Answering System

• DOUBLY REPARAMETERIZED GRADIENT ESTIMATORS FOR MONTE CARLO OBJECTIVES

• Multi-Task Learning as Multi-Objective Optimization

• Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations

• Training Complex Models with Multi-Task Weak Supervision

• Reinforcement Learning for Improving Agent Design

• AutoLoss: Learning Discrete Schedules for Alternate Optimization

• h-DETACH: MODIFYING THE LSTM GRADIENT TO- WARDS BETTER OPTIMIZATION

• Inertial-aided Motion Deblurring with Deep Networks

• Probabilistic Meta-Representations Of Neural Networks

• POSEidon: Face-from-Depth for Driver Pose Estimation

• Design Inspiration from Generative Networks

• MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning

• Gradient Descent Provably Optimizes Over-parameterized Neural Networks

• MODELING UNCERTAINTY WITH HEDGED INSTANCE EMBEDDING

• ADVERSARIAL AUDIO SYNTHESIS

• SET TRANSFORMER

• A deep learning pipeline for product recognition in store shelves

• OPTIMAL COMPLETION DISTILLATION FOR SEQUENCE LEARNING

• Verification for Machine Learning, Autonomy, and Neural Networks Survey

• UNSUPERVISED LEARNING VIA META-LEARNING

• MODULATED VARIATIONAL AUTO-ENCODERS FOR MANY-TO-MANY MUSICAL TIMBRE TRANSFER

• Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

• Learning to Remember, Forget, and Ignore using Attention Control in Memory

• GENERATIVE ENSEMBLES FOR ROBUST ANOMALY DETECTION

• AlphaSeq: Sequence Discovery with Deep Reinforcement Learning

• Taming VAEs

• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

• VARIATIONAL DISCRIMINATOR BOTTLENECK: IMPROVING IMITATION LEARNING, INVERSE RL, AND GANS BY CONSTRAINING INFORMATION FLOW

• Few-Shot Goal Inference for Visuomotor Learning and Planning

• Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

• Cellular automata as convolutional neural networks

• Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

• DEEP GRAPH INFOMAX

• Net2Vec: Quantifying and Explaining how Concepts are Encoded by Filters in Deep Neural Networks

• Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals

• Explainable PCGML via Game Design Patterns

• Graph Convolution over Pruned Dependency Trees Improves Relation Extraction

• COMPUTATIONAL POWER AND THE SOCIAL IMPACT OF ARTIFICIAL INTELLIGENCE

• The Mechanics of n-Player Differentiable Games

• Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing

• A Survey of Learning Causality with Data: Problems and Methods

• Ghost imaging with the human eye

• Understanding Convolutional Neural Networks for Text Classification

• HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples

• Learning Quickly to Plan Quickly Using Modular Meta-Learning

• Solving Large Extensive-Form Games with Strategy Constraints

• A Practical Guide to Training Restricted Boltzmann Machines

• Introduction to astroML: Machine Learning for Astrophysics

• Why does deep and cheap learning work so well?

• CosmoFlow: Using Deep Learning to Learn the Universe at Scale

• Representation Learning: A Review and New Perspectives

• NLP's ImageNet moment has arrived

• Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation

• Causal inference and the data-fusion problem

• A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System

• Phrase-Based & Neural Unsupervised Machine Translation

• Computational Theories of Curiosity-Driven Learning

• Long short-term memory and learning-to-learn in networks of spiking neurons

• Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering

• Towards Better Interpretability in Deep Q-Networks

• Automatic Judgment Prediction via Legal Reading Comprehension

• Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

• Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning

• TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game

• Quantum Statistics-Inspired Neural Attention

• The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

• Moments in Time Dataset: one million videos for event understanding

• Visual Diagnostics for Deep Reinforcement Learning Policy Development

• NiftyNet: a deep-learning platform for medical imaging

• Towards Better Interpretability in Deep Q-Networks

• Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts

• The Seven Tools of Causal Inference with Reflections on Machine Learning

• Transparency and Explanation in Deep Reinforcement Learning Neural Networks

• Deep Learning for Generic Object Detection: A Survey

• CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning

• Model-Based Reinforcement Learning via Meta-Policy Optimization

• SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning

• Revisiting Character-Based Neural Machine Translation with Capacity and Compression

• Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

• Learning Category-Specific Mesh Reconstruction from Image Collections

• Question Answering by Reasoning Across Documents with Graph Convolutional Networks

• Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity

• Noise2Noise: Learning Image Restoration without Clean Data

• Towards Asynchronous Motor Imagery-Based Brain-Computer Interfaces: a joint training scheme using deep learning

• Weisfeiler-Lehman Neural Machine for Link Prediction

• Modeling Empathy and Distress in Reaction to News Stories

• Reinforcement Learning under Threats

• Interpretation of Natural Language Rules in Conversational Machine Reading

• Entropy and Graph Energy of Complex Networks

• Dual Ask-Answer Network for Machine Reading Comprehension

• How to Combine Tree-Search Methods in Reinforcement Learning

• Dendritic error backpropagation in deep cortical microcircuits

• Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information

• A Neural Model of Adaptation in Reading

• Twin-GAN – Unpaired Cross-Domain Image Translation with Weight-Sharing GANs

• Time-Agnostic Prediction: Predicting Predictable Video Frames

• A Tree-based Decoder for Neural Machine Translation

• Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization

• Towards Neural Theorem Proving at Scale

• Supervised Deep Sparse Coding Networks

• Manipulating A ributes of Natural Scenes via Hallucination

• Implicit Regularization in Deep Learning

• Wasserstein is all you need

• robot gym: accelerated robot training through simulation in the cloud with ROS and Gazebo

• Norm matters: efficient and accurate normalization schemes in deep networks

• Unsupervised Learning of Syntactic Structure with Invertible Neural Projections

• NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage

• An elementary introduction to information geometry

• Improving Abstraction in Text Summarization

• Contextual Parameter Generation for Universal Neural Machine Translation

• Hybrid optical-electronic convolutional neural networks with optimized dffractive optics for image classification

• Style Transfer as Unsupervised Machine Translation

• LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations

• Sleep-Wake Classification Via Quantifying Heart Rate Variability By Convolutional Neural Network

• TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks

• The relativistic discriminator: a key element missing from standard GAN

• Transfer Learning for Estimating Causal Effects using Neural Networks

• Deep Convolutional Networks as shallow Gaussian Processes

• Everybody Dance Now

• Can 3D Pose be Learned from 2D Projections Alone?

• Relational inductive biases, deep learning, and graph networks

• SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors

• Neural Body Fitting: Unifying Deep Learning and Model-Based Human Pose and Shape Estimation

• Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval

• Toward Domain-Invariant Speech Recognition Via Large Scale Training

• Analyzing Inverse Problems with Invertible Neural Networks

• Joint Training of Low-Precision Neural Network with Quantization Interval Parameters

• An Auto-tuning Framework for Autonomous Vehicles

• 3D Shape Perception from Monocular Vision, Touch, and Shape Priors

• On the Decision Boundary of Deep Neural Networks

• Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction

• Anatomy Of High-Performance Deep Learning Convolutions On SIMD Architectures

• Deep EHR: Chronic Disease Prediction Using Medical Notes

• Classification and Geometry of General Perceptual Manifolds

• Skill Rating for Generative Models

• Recurrent computations for visual pattern completion

• Item Recommendation with Variational Autoencoders and Heterogeneous Priors

• CosmoFlow: Using Deep Learning to Learn the Universe at Scale

• Toward Scale-Invariance and Position-Sensitive Region Proposal Networks

• How Complex is your classification problem? A survey on measuring classification complexity

• Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

• Policy Optimization as Wasserstein Gradient Flows

• DeepTAM: Deep Tracking and Mapping

• A Survey on Deep Transfer Learning

• Rethinking Numerical Representations for Deep Neural Networks

• Backprop Evolution

• HELIX: Accelerating Human-in-the-loop Machine Learning

• Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

• Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data

• Hidden Technical Debt in Machine Learning Systems

• Online Adaptative Curriculum Learning for GANs

• GuiTeNet: A graphical user interface for tensor networks

• Speaker Recognition from Raw Waveform with Sincnet

• Blind Justice: Fairness with Encrypted Sensitive Attributes

• Neural Joking Machine: Humorous image captioning

• Neural Arithmetic Logic Units

• Rank and Rate: Multi-task Learning for Recommender Systems

• DeepProbLog: Neural Probabilistic Logic Programming

• YOLO Net on iOS

• TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

• The challenge of realistic music generation: modelling raw audio at scale

• Meta-learning autoencoders for few-shot prediction

• Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes

• Discovering physical concepts with neural networks

• Painting Outside the Box: Image Outpainting with GANs

• Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer

• Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors

• Representation Learning with Contrastive Predictive Coding

• Variational Memory Encoder-Decoder

• The Variational Homoencoder: Learning to learn high capacity generative models from few examples

• Implementing Neural Turing Machines

• DARTS: Differentiable Architecture Search

• Variational Bayesian Reinforcement Learning with Regret Bounds

• Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs

• Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

• Compositional GAN: Learning Conditional Image Composition

• Adversarial Reprogramming of Neural Networks

• CNN Features off-the-shelf: an Astounding Baseline for Recognition

• Neural Motifs: Scene Graph Parsing with Global Context

• Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies

• Glow: Generative Flow with Invertible 1×1 Convolutions

• Noise2Noise: Learning Image Restoration without Clean Data

• Representation Learning with Contrastive Predictive Coding

• An Opinionated Introduction to AutoML and Neural Architecture Search

• ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

• IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

• Backplay: ‘Man muss immer umkehren’

• Meta-Learning With Latent Embedding Optimization

• TequilaGAN: How to easily identify GAN samples

• The Singular Values of Convolutional Layers

• TherML: Thermodynamics of Machine Learning

• Variance Networks: When Expectation Does Not Meet Your Expectations

• Task-Driven Convolutional Recurrent Models of the Visual System

• Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning

• Deep Neural Networks as Gaussian Processes

• Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization

• Dynamic Integration of Background Knowledge in Neural NLU Systems

• The relativistic discriminator: a key element missing from standard GAN

• Accurate Uncertainties for Deep Learning Using Calibrated Regression

• ModaNet: A Large-Scale Street Fashion Dataset with Polygon Annotations

• The challenge of realistic music generation: modelling raw audio at scale

• Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding

• Tracking Emerges by Colorizing Videos

• Probabilistic FastText for Multi-Sense Word Embeddings

• Differentiable Compositional Kernel Learning for Gaussian Processes

• Learning Dynamics of Linear Denoising Autoencoders

• Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks

• Bilinear Attention Networks

• Global Pose Estimation with an Attention-based Recurrent Network

• SparseMAP: Differentiable Sparse Structured Inference

• Neural Ordinary Differential Equations

• Zero-Shot Visual Imitation

• Multimodal Unsupervised Image-to-Image Translation

• Deep Painterly Harmonization

• A Probabilistic U-Net for Segmentation of Ambiguous Images

• DeViSE: A Deep Visual-Semantic Embedding Model

• Through-Wall Human Pose Estimation Using Radio Signals

• Adversarial Meta-Learning

• Fighting Fake News: Image Splice Detection via Learned Self-Consistency

• GAN Q-learning

• A Simple Method for Commonsense Reasoning

• Semi-supervised Sequence Learning

• Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

• Learning New Physics from a Machine

• Spectral Inference Networks: Unifying Spectral Methods With Deep Learning

• Probabilistic Model-Agnostic Meta-Learning

• Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

• Relational inductive biases, deep learning, and graph networks

• DetNet: A Backbone network for Object Detection

• Playing Atari with Six Neurons

• Multi-Resolution 3D Convolutional Neural Networks for Object Recognition

• Do CIFAR-10 Classifiers Generalize to CIFAR-10?

• Supervised Open Information Extraction

• Learning a Latent Space of Multitrack Measures

• Structure from noise: Mental errors yield abstract represen- tations of events

• Learning to Extract Coherent Summary via Deep Reinforcement Learning

• MolGAN: An implicit generative model for small molecular graphs

• Approximate Random Dropout

• Variational Inference for Data-Efficient Model Learning in POMDPs

• Learning a Prior over Intent via Meta-Inverse Reinforcement Learning

• Neural Discrete Representation Learning

• Aggregated Residual Transformations for Deep Neural Networks

• Playing hard exploration games by watching YouTube

• Capsules for Object Segmentation

• Learning to Transduce with Unbounded Memory

• Visceral Machines: Reinforcement Learning with Intrinsic Rewards that Mimic the Human Nervous System

• Neural Collaborative Filtering

• Altered Fingerprints: Detection and Localization

• Fast animal pose estimation using deep neural networks

• DOOM Level Generation using Generative Adversarial Networks

• Pixel Recurrent Neural Networks

• Solving the Rubik’s Cube Without Human Knowledge

• Adding One Neuron Can Eliminate All Bad Local Minima

• Synthesizing Images of Humans in Unseen Poses

• Depth-Limited Solving for Imperfect-Information Games

• AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search

• Semantic Autoencoder for Zero-shot Learning

• Progress & Compress: A scalable framework for continual learning

• Regularization Learning Networks

• On the Practical Computational Power of Finite Precision RNNs for Language Recognition

• Learning Intrinsic Image Decomposition from Watching the World

• Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images

• Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond

• Learning Intrinsic Image Decomposition from Watching the World

• Time Series Epenthesis: Clustering Time Series Streams Requires Ignoring Some Data

• Realistic Evaluation of Semi-Supervised Learning Algorithms

• Multiclass Transfer Learning from Unconstrained Priors

• Multi-column Deep Neural Networks for Image Classification

• Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks

• A Unified Framework of Deep Neural Networks by Capsules

• Learning to Separate Object Sounds by Watching Unlabeled Video

• Bayesian Robust Tensor Factorization for Incomplete Multiway Data

• Quantization Mimic: Towards Very Tiny CNN for Object Detection

• Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet

• Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs

• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots

• Capsule networks for low-data transfer learning

• Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

• Towards Symbolic Reinforcement Learning with Common Sense

• Compositional Attention Networks for Machine Reasoning

• An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

• Multiagent Soft Q-Learning

• Convergence of Value Aggregation for Imitation Learning

• Why does deep and cheap learning work so well?

• node2vec: Scalable Feature Learning for Networks

• Realistic Evaluation of Deep Semi-Supervised Learning Algorithms

• Activity Detection with Latent Sub-event Hierarchy Learning

• Variational Knowledge Graph Reasoning

• Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation

• MergeNet: A Deep Net Architecture for Small Obstacle Discovery

• Harmonic Networks: Deep Translation and Rotation Equivariance

• ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing

• Deep Object Co-Segmentation

• Detecting Malicious PowerShell Commands using Deep Neural Networks

• Multimodal Unsupervised Image-to-Image Translation

• Regularizing Deep Networks by Modeling and Predicting Label Structure

• Recurrent Recommender Networks

• Generative Adversarial Networks for Extreme Learned Image Compression

• Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts

• Neural Aesthetic Image Reviewer

• Manifold Alignment

• A General Framework for Manifold Alignment

• Differentiable plasticity: training plastic neural networks with backpropagation

• Spatial Graph Convolutions for Drug Discovery

• sense2vec - A Fast and Accurate Method for Word Sense Disambiguation In Neural Word Embeddings

• Algebraic Machine Learning

• Network In Network

• Neural Voice Cloning with a Few Samples

• L4: Practical loss-based stepsize adaptation for deep learning

• OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

• Complex-YOLO: An Euler-Region-Proposal for Real-time 3D Object Detection on Point Clouds

• code2vec: Learning Distributed Representations of Code

• Human Semantic Parsing for Person Re-identification

• Group Normalization

• Some Theoretical Properties of GANs

• Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings

• Deep Spatial Pyramid for Person Re-identification

• Deep Binaries: Encoding Semantic-Rich Cues for Efficient Textual-Visual Cross Retrieval

• Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning

• Simple random search provides a competitive approach to reinforcement learning

• ChatPainter: Improving Text to Image Generation using Dialogue

• Deep Metric Learning with Angular Loss

• Deep Metric Learning via Lifted Structured Feature Embedding

• Hard Negative Mining for Metric Learning Based Zero-Shot Classification

• Triplet-based Deep Similarity Learning for Person Re-Identification

• How to Train Triplet Networks with 100K Identities?

• Deep Metric Learning Using Triplet Network

• Robust Monitoring of Time Series with Application to Fraud Detection

• Neural Machine Translation by Jointly Learning to Align and Translate

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

• Sequence to Sequence Learning with Neural Networks

• Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

• Extracting Domain Invariant Features By Unsupervised Learning For Robust Automatic Speech Recognition

• Compositional Attention Networks For Machine Reasoning

• Kickstarting Deep Reinforcement Learning

• Learning a Parametric Embedding by Preserving Local Structure

• Unsupervised Document Embedding With CNNs

• Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives

• Advances in Variational Inference

• Evolutionary Generative Adversarial Networks

• Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

• The Building Blocks of Interpretability

• End-to-end Deep Image Reconstruction From Human Brain Activity

• TSSD: Temporal Single-Shot Detector Based on Attention and LSTM for Robotic Intelligent Perception

• Brain Tumor Type Classification via Capsule Networks

• Recent Advances in Recurrent Neural Networks

• Bayesian Inverse Reinforcement Learning

• Generative Adversarial Text to Image Synthesis

• One Big Net For Everything

• Deep Multimodal Learning For Emotion Recognition In Spoken Language

• A Neural Parametric Singing Synthesizer

• On Characterizing the Capacity of Neural Networks using Algebraic Topology

• Adversarial Examples that Fool both Human and Computer Vision

• Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• Deep Networks with Stochastic Depth

• Wide Residual Networks

• Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

• Supervised Dimensionality Reduction via Distance Correlation Maximization

• Faster gaze prediction with dense networks and Fisher pruning

• Classification and clustering for samples of event time data using non-homogeneous Poisson process models

• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

• Countering Adversarial Images Using Input Transformations

• The Matrix Calculus You Need For Deep Learning

• Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain Features

• Regularized Evolution for Image Classifier Architecture Search

• Quasi-Recurrent Neural Networks

• From Micro to Macro: Data Driven Phenotyping by Densification of Longitudinal Electronic Medical Records

• Speed/accuracy trade-offs for modern convolutional object detectors

• Nested LSTMs

• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

• Audio Adversarial Examples: Targeted Attacks on Speech-to-Text

• Deep Multi-Camera People Detection

• Learning to Segment Every Thing

• ComboGAN: Unrestrained Scalability for Image Domain Translation

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

• Understanding deep learning requires rethinking generalization

• Bayesian GAN

• Approximations by superpositions of sigmoidal functions

• Are GANs Created Equal? A Large-Scale Study

• Learning to Segment Every Thing

• A Neural Algorithm of Artistic Style

• VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

• Generalization in Deep Learning

• DeepFace: Closing the Gap to Human-Level Performance in Face Verification

• Feature Space Optimization for Semantic Video Segmentation

• Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

• Pyramid Scene Parsing Network

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

• Fast R-CNN

• Rich feature hierarchies for accurate object detection and semantic segmentation

• Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

• Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

• Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

• 3D Object Proposals using Stereo Imagery for Accurate Object Class Detection

• Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

• The Cityscapes Dataset for Semantic Urban Scene Understanding

• Superpixel Convolutional Networks Using Bilateral Inceptions

• Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control

• Multi-View 3D Object Detection Network for Autonomous Driving

• High-Resolution Semantic Labeling with Convolutional Neural Networks

• InstanceCut: from Edges to Instances with MultiCut

• End to End Learning for Self-Driving Cars

• Instance-aware Semantic Segmentation via Multi-task Network Cascades

• Globally and Locally Consistent Image Completion

• Vehicle Detection from 3D Lidar Using Fully Convolutional Network

• The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

• Synthesizing Robust Adversarial Examples

• OctNet: Learning Deep 3D Representations at High Resolutions

• EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

• Hierarchical Representations for Efficient Architecture Search

• Squeeze-and-Excitation Networks

• Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)

• Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum

• Deformable Convolutional Networks

• Soft-NMS -- Improving Object Detection With One Line of Code

• A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

• Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction

• Dynamic Routing Between Capsules

• StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation

• Semantic Image Segmentation with Deep Cconvolutional Nets and Fully Connected CRFS

• A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

• Hands Deep in Deep Learning for Hand Pose Estimation

• Auto-Conditioned LSTM Network for Extended Complex Human Motion Synthesis

• DeepXplore: Automated Whitebox Testing of Deep Learning Systems

• Orientation-boosted Voxel Nets for 3D Object Recognition

• Real-time grasp detection using convolutional neural networks

• On the difficulty of training Recurrent Neural Networks

• Learning real manipulation tasks from virtual demonstrations using LSTM

• A Neural Network-Based Approach for Trajectory Planning in Robot–Human Handover Tasks

• Swish: a Self-Gated Activation Function

• SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks

• Visual Servoing from Deep Neural Networks

• UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning

• Deep 6-DOF Tracking

• WaveNet: A Generative Model for Raw Audio

• The Microsoft 2016 Conversational Speech Recognition System

• Self-Normalizing Neural Networks

• Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours

• Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection

• 3D Simulated Robot Manipulation Using Deep Reinforcement Learning

• Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not

• 3D Simulation for Robot Arm Control with Deep Q-Learning

• End-to-End Training of Deep Visuomotor Policies

• Leveraging Deep Reinforcement Learning for Reaching Robotic Tasks

• 100-epoch ImageNet Training with AlexNet in 24 Minutes

• Imagenet classification with deep convolutional neural networks

• Deep learning for detecting robotic grasps

• Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration

• Vision-based Robotic Arm Imitation by Human Gesture

• Supervision via Competition: Robot Adversaries for Learning Tasks

• Deep Visual Foresight for Planning Robot Motion

• Dermatologist-level classification of skin cancer with deep neural networks

• Brain tumor segmentation with deep neural networks

• SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

• Understanding image representations by measuring their equivariance and equivalence

• Understanding Deep Image Representations by Inverting Them

• YOLO9000: Better, Faster, Stronger

• Maxout Networks

• Going deeper with convolutions

• Training Very Deep Networks

• Batch normalization: Accelerating deep network training by reducing internal covariate shift

• Adam: A Method For Stochastic Optimization

• Dropout: A simple way to prevent neural networks from overfitting

• Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

• Improving neural networks by preventing co-adaptation of feature detectors

• Fully Convolutional Networks for Semantic Segmentation

• PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization

• Systematic Testing of Convolutional Neural Networks for Autonomous Driving

• Deep Visual-Semantic Alignments for Generating Image Descriptions

• Audio Super Resolution using Neural Networks

• Very Deep Convolutional Networks for Large-scale Image Recognition

• Clinical Intervention Prediction and Understanding with Deep Neural Networks

• Toward Geometric Deep SLAM

• Robust Physical-World Attacks on Machine Learning Models

• Speeding up Semantic Segmentation for Autonomous Driving

• Connecting Images and Natural Language (Andrej Karpathy's Dissertation)

• Neocognitron: A Self-organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position

• Gradient-Based Learning Applied to Document Recognition

• Improving Neural Networks by Preventing Co-adaptation of Feature Detectors

• Visualizing and Understanding Convolutional Networks

• End to End Learning for Self-Driving Cars

• U-Net: Convolutional Networks for Biomedical Image Segmentation

• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

• Explaining and Harnessing Adversarial Examples

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

• Why Does Deep and Cheap Learning Work So Well?

• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

• Modeling Smooth Backgrounds & Generic Localized Signals with Gaussian Processes (Kyle)

• Adversarial Variational Optimization of Non-Differentiable Simulators (Kyle)

Online Learning Resources

• Forecasting the weather with neural ODEs

• High-Fidelity Generative Image Compression

• HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

• 3D Photography using Context-aware Layered Depth Inpainting

• A PyTorch implementation of NeRF

• Keypoint Detection using Mask R-CNN

• Graph Representation Learning

• Mathematicians propose new way of using neural networks to work with noisy, high-dimensional data

• The Evolution and Core Concepts of Deep Learning & Neural Networks

• Parallel and Distributed Deep Learning: A Survey

• Deep Reinforcement Learning Based Trading Application at JP Morgan Chase

• What My Deep Model Doesn't Know...

• A Visual Exploration of Gaussian Processes

• 12 Steps to Applied AI

• Protect your Deep Neural Network by Embedding Watermarks!

• LEARNING HIERARCHICAL PRIORS IN VAES

• Unsupervised Cross-lingual Representation Learning

• Differentiable Convex Optimization Layers

• Machine Learning From Scratch

• Attention? Attention!

• Continuous Delivery for Machine Learning

• Reinforcement Learning — Policy Approximation

• Notes on iMAML: Meta-Learning with Implicit Gradients

• Generating Piano Music with Transformer

• RecoTour II: neural recommendation algorithms

• Randomly wired neural networks and state-of-the-art accuracy? Yes it works.

• Handling imbalanced datasets in machine learning

• Building Custom Deep Learning Based OCR models

• The Beauty of Functional Languages in Deep Learning — Clojure and Haskell

• How 20th Century Fox uses ML to predict a movie audience

• Pytorch-Transformers

• Real-Time Voice Cloning

• Awesome Graph Classification

• A Visual Intro to NumPy and Data Representation

• Human in the Loop: Deep Learning without Wasteful Labelling

• Review: 3D U-Net+ResNet — Volumetric Convolutions + Long & Short Residual Connections (Biomedical Image Segmentation)

• Pricing Segmentation and Analytics

• A Recipe for Training Neural Networks

• Extreme Rare Event Classification using Autoencoders in Keras

• A 2017 Guide to Semantic Segmentation with Deep Learning

• Inside 245-5D

• Weak Supervision: A New Programming Paradigm for Machine Learning

• Rules of Machine Learning: Best Practices for ML Engineering

• Play with Generative Adversarial Networks (GANs) in your browser!

• Troubleshooting Deep Neural Networks

• Generalized Language Models

• ENet — A Deep Neural Architecture for Real-Time Semantic Segmentation

• Word Bags vs Word Sequences for Text Classification

• How to visualize convolutional features in 40 lines of code

• A (Long) Peek into Reinforcement Learning

• Neural Networks, Manifolds, and Topology

• Adversarial learning meets graphs

• Hierarchical Bayesian Neural Networks with Informative Priors

• From GAN to WGAN

• Interpretable Machine Learning

• A sane introduction to maximum likelihood estimation (MLE) and maximum a posteriori (MAP)

• How to recognize fake AI-generated images

• Deep Learning with Electronic Health Record (EHR) Systems

• Lossless Triplet loss

• State of Deep Learning : H2 2018 Review

• A Few Unusual Autoencoders

• WaveNet: Google Assistant’s Voice Synthesizer

• Unsupervised Neural Networks Fight in a Minimax Game

• Preprocessing for deep learning: from covariance matrix to image whitening

• Back-propagation - Math Simplified

• Mastering The New Generation of Gradient Boosting

• InfoGAN: using the variational bound on mutual information (twice)

• Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees

• Reinforcement Learning with Prediction-Based Rewards

• Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups

• Drilling Down on Depth Sensing and Deep Learning

• Practical Text Classification With Python and Keras

• Monte Carlo Tree Search – beginners guide

• Deep learning based Object Detection and Instance Segmentation using Mask R-CNN in OpenCV (Python / C++)

• Flow-based Deep Generative Models

• Industrial AI: BHGE’s Physics-based, Probabilistic Deep Learning Using TensorFlow Probability — Part 1

• Machine Learning for Visualization

• PhD Thesis: Geometry and Uncertainty in Deep Learning for Computer Vision

• Generative Neural Machine Translation

• UNDERSTANDING FORMULAS FOR COMMON STATISTICS

• Automatic Kernel Optimization for Deep Learning on All Hardware Platforms

• TensorFlow in Anaconda

• Simple Beginner’s guide to Reinforcement Learning & its implementation

• How to code The Transformer in Pytorch

• Why Momentum Really Works

• CLARA: A NEURAL NET MUSIC GENERATOR

• Gradient Boosting and XGBoost

• AUTOML: METHODS, SYSTEMS, CHALLENGES (NEW BOOK)

• r/MachinesLearn

• Efficient tuning of online systems using Bayesian optimization

• Neural Processes in PyTorch

• A (Long) Peek into Reinforcement Learning

• Different ways of using MCMC algorithms

• Machine Learning From Scratch

• Building a text classification model with TensorFlow Hub and Estimators

• Learning where you are looking at (in the browser)

• We trained an algorithm to detect cancer in just two hours

• "Dance with Ensemble" Sharing Thread

• Neural Nets for Generating Music

• Collaborative Filtering: From Shallow to Deep Learning

• Best Practices for ML Engineering

• Deep Learning for Recommender Systems

• A (Long) Peek into Reinforcement Learning

• From GAN to WGAN

• Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)

• Neural Processes as distributions over functions

• Best Practices for ML Engineering

• On Chomsky and the Two Cultures of Statistical Learning

• Automated Machine Learning Hyperparameter Tuning in Python

• Rules of Machine Learning: Best Practices for ML Engineering

• Fast Near-Duplicate Image Search using Locality Sensitive Hashing

• Loc2Vec: Learning location embeddings with triplet-loss networks

• Using Word2Vec for Better Embeddings of Categorical Features

• Advances in Semantic Textual Similarity

• Privacy and machine learning: two unexpected allies?

• Information Theory of Neural Networks

• Stochastic Weight Averaging — a New Way to Get State of the Art Results in Deep Learning

• node2vec: Embeddings for Graph Data

• The fall of RNN / LSTM

• wav2letter: A Facebook AI Research(FAIR) Automatic Speech Recognition Toolkit

• Using Evolutionary AutoML to Discover Neural Network Architectures

• How to use Dataset in TensorFlow

• Using Tensorflow Object Detection to do Pixel Wise Classification

• Character-Level Language Model

• Understanding GANs through a statistical divergence perspective

• Deep Learning Book Series

• Understanding and optimizing GANs (Going back to first principles)

• Neural Artistic Style Transfer: A Comprehensive Look

• The Building Blocks of Interpretability

• Understanding Learning Rates and How It Improves Performance in Deep Learning

• Only Numpy: Implementing Highway Network, OOP approach with Mini Batch with Interactive Code

• Only Numpy: NIPS 2017 - Implementing Dilated Recurrent Neural Networks with Interactive Code

• Inverse Reinforcement Learning Pt. I

• Deep Learning in the Physical Sciences (Kyle)

• A Visual Guide to Evolution Strategies

• Terrence Parr and Jeremy Howard's math primer

• Annotating Large Datasets with the TensorFlow Object Detection API

• Deep Learning Is Not Good Enough, We Need Bayesian Deep Learning for Safe AI

• The Matrix Calculus You Need For Deep Learning

• Fast AI

• A visual introduction to machine learning

• SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

• Only Numpy: Implementing GAN (General Adversarial Networks) and Adam Optimizer using Numpy with Interactive Code

• How to build your own AlphaZero AI using Python and Keras

• How Neural Networks Are Trained

• Inside the black box

• Inside the black box 2

• Geometric Deep Learning

• How To Unit Test Machine Learning Code

• Neuroevolution

• A set of Deep Reinforcement Learning Agents implemented in Tensorflow

• Deep Learning Adversarial Examples – Clarifying Misconceptions

• Understanding Autoencoders using Tensorflow

• Deep RL Bootcamp

• How to Define an Encoder-Decoder Sequence-to-Sequence Model for Neural Machine Translation in Keras

• Feature Visualization - How neural networks build up their understanding of images

• How to Develop a Character-Based Neural Language Model in Keras

• How-To: Multi-GPU training with Keras, Python, and deep learning

• Best Practices for Document Classification with Deep Learning

• GANs are Broken in More than One Way: The Numerics of GANs

• 37 Reasons why your Neural Network is not working

• Understanding LSTM Networks

• Michael Nielsen's Neural Networks and Deep Learning

• Leonardo Araujo dos Santos' Artificial Intelligence and Deep Learning GitBook

• CS231n Convolutional Neural Networks for Visual Recognition

• Deep Learning without Backpropagation (synthetic gradients)

• A quick complete tutorial to save and restore Tensorflow models

News

• Practical AI

• The Wild Week in AI

• Ferenc Huszár's inFERENCe

• AAAI's AITopics Feed

Interesting Online Projects and Tools

• faiss

• NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch

• DeOldify: A Deep Learning based project for colorizing and restoring old images

• Talos: Hyperparameter Optimization for Keras Models

• GPyTorch

• arviz

• torchgeometry

• Awesome Human Pose Estimation

• Real-time pix2pix implementation with Unity

• PaintsTransfer-Euclid

• Semantic Soft Segmentation, ACM SIGGRAPH 2018

• Research2Vec

• Demystifying Convolutional Neural Networks

• GAN Lab

• Raincloud plots: a multi-platform tool for robust data visualization

• Pix2Pix: An example with tf.keras and eager

• pytorch-Deep-Learning-Minicourse

• Neural BeatBox

• GuiTeNet

• The Mathematics of 2048: Optimal Play with Markov Decision Processes

• Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models

• Army develops face recognition technology that works in the dark

• Text Classification with TensorFlow Estimators

• MUNIT: Multimodal UNsupervised Image-to-image Translation

• wav2letter

• Generative Adversarial Networks for Extreme Learned Image Compression

• Splash of Color: Instance Segmentation with Mask R-CNN and TensorFlow

• Machine Learning is Fun Part 6: How to do Speech Recognition with Deep Learning

• Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks

• “Cracking” Morse code with RNNs

• Releasing “Supervisely Person” dataset for teaching machines to segment humans

• Only Numpy: Implementing Simple ResNet (Deep Networks with Stochastic Depth) for MNIST Classification with Interactive Code

• Only Numpy: Implementing Mini VGG (VGG 7) and SoftMax Layer with Interactive Code

• A Visual Guide to Evolution Strategies

• An overview of word embeddings and their connection to distributional semantic models

• Using OpenAI Gym with ROS

• Countering Adversarial Images Using Input Transformations

• tf-faster-rcnn

• Thinc

• Explosion.ai

• Deep Time-to-Failure

• Implementing GAN

• Detectron

• NVIDIA Researchers Showcase Major Advances in Deep Learning at NIPS

• Bayesian Generative Adversarial Networks in Tensorflow

• A neural network that solves polynomials

• GAN Playground - Explore Generative Adversarial Nets in your Browser

• Deep RL Bootcamp

• Demystifying Deep Reinforcement Learning

• Freeze Tensorflow models and serve on web

• A Tensorflow implementation of CapsNet(Capsules Net) in Hinton's paper Dynamic Routing Between Capsules

• GAN Playground - Explore Generative Adversarial Nets in your Browser

• Mask R-CNN for Object Detection and Segmentation

• Word2vec Tutorial

• Python gensim Word2Vec tutorial with TensorFlow and Keras

• A Word2Vec Keras tutorial

• Colorizing B&W Photos with Neural Networks

• Lyrebird

• Background removal with deep learning

• Teaching a car to avoid obstacles using Reinforcement Learning

• Audio Super Resolution Using Neural Networks

• Floyd

• Floyd All-in-one Docker Image

• Deep Learning Gallery

• DCGAN-tensorflow

• Image-to-image Demo

• Creating Photorealistic Images with Neural Networks and a Gameboy Camera

• Self-driving car in a simulator with A Tiny Neural Network

• Alex Champandard's Neural Doodle

• Alex Champandard's Neural Enhance

• Will Roscoe's Lane Following Autopilot with Keras & Tensorflow

• Julien Despois' Latent Space Visualization

• Alexia J-M's MEOW Generator (GAN applied to cat faces)

Data Sets

• Open Images Dataset V4

• ADEK20K

• Udacity's annotated driving dataset

• Stanford Car Data Set

• Comprehensive Car Data Set

• Playing for Data: Ground Truth from Computer Games

Support For Ideas

• AI Grant

• NVIDIA Inception Program

Online Programs of Study

• A high bias low-variance introduction to Machine Learning for physicists

• Some Thoughts on Mid-Career Switching Into Data Science

• Udacity

• Andrew Ng's deeplearning.ai

• Data School

Process

• Rules of Machine Learning: Best Practices for ML Engineering