204 citations · 369 across the 17 of their papers we have counts for
7 papers · 1 filter
Measuring Systematic Generalization in Neural Proof Generation with Transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy +1
We are interested in understanding how well Transformer language models (TLMs) can perform reasoning tasks when trained on knowledge encoded in the form of natural language. We inv…
A Closer Look at Codistillation for Distributed Training
Shagun Sodhani, Olivier Delalleau, Mahmoud Assran +3
Codistillation has been proposed as a mechanism to share knowledge among concurrently trained models by encouraging them to represent the same function through an auxiliary loss. T…
Ideas for Improving the Field of Machine Learning: Summarizing Discussion from the NeurIPS 2019 Retrospectives Workshop
Shagun Sodhani, Mayoore S. Jaiswal, Lauren Baker +5
This report documents ideas for improving the field of machine learning, which arose from discussions at the ML Retrospectives workshop at NeurIPS 2019. The goal of the report is t…
Probing Linguistic Systematicity
Emily Goodwin, Koustuv Sinha, Timothy J. O'Donnell
Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicity; generalizing such that units like words make con…
Learning an Unreferenced Metric for Online Dialogue Evaluation
Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang +3
Evaluating the quality of a dialogue interaction between two agents is a difficult task, especially in open-domain chit-chat style dialogue. There have been recent efforts to devel…
Evaluating Logical Generalization in Graph Neural Networks
Koustuv Sinha, Shagun Sodhani, Joelle Pineau +1
Recent research has highlighted the role of relational inductive biases in building learning agents that can generalize and reason in a compositional manner. However, while relatio…