research

Research projects in Neural Structured Learning

Note that these research projects are not included in the prebuilt NSL pip package.

Low-Dimensional Hyperbolic Knowledge Graph Embeddings

The implementations of Low-Dimensional Hyperbolic Knowledge Graph Embeddings [3] are provided in the kg_hyp_emb folder on a strict "as is" basis, without warranties or conditions of any kind. Also, these implementations may not be compatible with certain TensorFlow versions or Python versions.

[3] Chami, Ines, et al. "Low-Dimensional Hyperbolic Knowledge Graph Embeddings." ACL 2020.

A2N: Attending to Neighbors for Knowledge Graph Inference

The implementations of A2N [2] are provided in the a2n folder on a strict "as is" basis, without warranties or conditions of any kind. Also, these implementations may not be compatible with certain TensorFlow versions or Python versions.

[2] T. Bansal, D. Juan, S. Ravi and A. McCallum. "A2N: Attending to Neighbors for Knowledge Graph Inference." ACL 2019

GAM: Graph Agreement Models for Semi-Supervised Learning

The implementations of Graph Agreement Models (GAMs) [1] are provided in the gam folder on a strict "as is" basis, without warranties or conditions of any kind. Also, these implementations may not be compatible with certain TensorFlow versions or Python versions.

[1] O. Stretcu, K. Viswanathan, D. Movshovitz-Attias, E.A. Platanios, S. Ravi, A. Tomkins. "Graph Agreement Models for Semi-Supervised Learning." NeurIPS 2019

Neural Clustering Processes

The implementations of Neural Clustering Process (NCP) [4] are provided in the neural_clustering folder on a strict "as is" basis, without warranties or conditions of any kind. Also, these implementations may not be compatible with certain TensorFlow versions or Python versions.

[4] A. Pakman, Y. Wang, C. Mitelut, J. Lee, L. Paninski. "Neural Clustering Processes." ICML 2020

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

The implementations of Denoised Smoothing [5] are provided in the third_party/denoised_smoothing folder on a strict "as is" basis, without warranties or conditions of any kind. Also, these implementations may not be compatible with certain TensorFlow versions or Python versions.

[5] H. Salman, M.Sun, C. Mitelut, G. Yang, A. Kapoor., Z. Kolter "Denoised Smoothing: A Provable Defense for Pretrained Classifiers." NeurIPS 2020