Contains information and resources relevant to TReNDS Reading Group (TRG)
A brown-bag (bring your own lunch) reading club at Trends Center held mainly for cross pollination of ideas and "out of the comfort zone" fun. We read and discuss papers, tutorials or packs of papers and ideas at once. The papers span all areas of machine learning but also venture into brain imaging and theories of how the brain works. The goal is to iteratively learn important concepts and stay current with the literature.
Please sign up for the mailing list or, if you’re a part of the TReNDS center, join the #reading_group channel on slack at trendscenter.slack.com
- 05/01/2020 - Shortcut Learning in Deep Neural Networks
- Meta-learning for neuroimaging
- What you wanted to know about ICA but were afraid to ask
- Normalizing Flows
- Time Varying Neural Networks
- Contemporary Ethical Issues in Data Science/Neuroimaging
- Information Theory for Deep Learning
- Word Embeddings (Word2Vec, GloVe, etc.)
- 04/24/2020 - Recurrent BackPropagation (cont.)
- 04/17/2020 - Recurrent BackPropagation
- 04/10/2020 - Gradient Surgery for Multi-task Learning
- 03/06/2020 - Sanity Checks for Saliency Maps
- 02/28/2020 - Complexity Control by Gradient Descent in Deep Networks
- 02/21/2020 - Dynamics of Learning in DNN (conclusion)
- 02/14/2020 - Dynamics of Learning in DNN (cont.)
- 02/07/2020 - Dynamics of Learning in DNN
- 01/31/2020 - SINDy
- 01/24/2020 - Neural Networks in System Identification
- 01/17/2020 - Hidden Markov Models (not discussed)
- 01/10/2020 - Reading group Planning for year 2020
- 12/13/2019 - Semi-supervised Representation Learning for Understanding Brain Dynamics
- 12/06/2019 - Demystifying Math behind Neural ODEs
- 11/22/2019 - Nonlinear Dimensionality Reduction Techniques
- 11/01/2019 - SPIRAL (Reinforced Adversarial Learning)
- 10/25/2019 - Deep Q-Network
- 10/18/2019 - Hidden Stratification
- 10/11/2019 - Neural ODEs (NODE)
- 10/04/2019 - Model (F)utility
- 09/27/2019 - Wasserstein GANs
- 09/20/2019 - Introduction to GANs
- 09/13/2019 - Karl Friston's FEP continued (Part 02)
- 09/06/2019 - Karl Friston's Free Energy Principle (Part 01)
- 08/30/2019 - Calibration of NN models
- 08/23/2019 - Model Interpretability
- 08/16/2019 - Summary of DLRL Summer School 2019
- 08/09/2019 - Variational Autoencoders (Part 02)
- 08/02/2019 - Variational Autoencoders (Part 01)
- 07/26/2019 - Attention in Deep Learning Models (Part 02)
- 07/19/2019 - Attention in Deep Learning Models (Part 01)
- 07/12/2019 - NLP successes of 2018: Transfer Learning
Note: Folders organized as TRG-yyyymmdd-topic