52 citations · 68 across the 2 of their papers we have counts for
6 papers
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Rajarshi Roy, Jonathan Raiman, Neel Kant +6
In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-perform…
End-to-End Training of Neural Retrievers for Open-Domain Question Answering
Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi +4
Recent work on training neural retrievers for open-domain question answering (OpenQA) has employed both supervised and unsupervised approaches. However, it remains unclear how unsu…
Synthetic Datasets for Neural Program Synthesis
Richard Shin, Neel Kant, Kavi Gupta +4
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e.g. input-output behavior. Many current approaches…
Adversarial Policies: Attacking Deep Reinforcement Learning
Adam Gleave, Michael Dennis, Cody Wild +3
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, a…
Practical Text Classification With Large Pre-Trained Language Models
Neel Kant, Raul Puri, Nikolai Yakovenko +1
Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data. We demonstrate that large-scale unsupervised langu…
Recent Advances in Neural Program Synthesis
Neel Kant
In recent years, deep learning has made tremendous progress in a number of fields that were previously out of reach for artificial intelligence. The successes in these problems has…