no code implementations • 20 Mar 2024 • Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin, Neel Kant, Kriti Aggarwal, Neha Manjunath, Debajyoti Datta, Zhengliang Liu, Jiayuan Ding, Sophia Busacca, Cezanne Bianco, Swapnil Sharma, Rae Lasko, Michelle Voisard, Sanchay Harneja, Darya Filippova, Gerry Meixiong, Kevin Cha, Amir Youssefi, Meyhaa Buvanesh, Howard Weingram, Sebastian Bierman-Lytle, Harpreet Singh Mangat, Kim Parikh, Saad Godil, Alex Miller
We train our models on proprietary data, clinical care plans, healthcare regulatory documents, medical manuals, and other medical reasoning documents.
no code implementations • 16 Nov 2023 • Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Polak Scowcroft, Neel Kant, Aidan Swope, Oleksii Kuchaiev
To alleviate this problem, we collect HelpSteer, a multi-attribute helpfulness dataset annotated for the various aspects that make responses helpful.
no code implementations • 14 May 2022 • Rajarshi Roy, Jonathan Raiman, Neel Kant, Ilyas Elkin, Robert Kirby, Michael Siu, Stuart Oberman, Saad Godil, Bryan Catanzaro
Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
2 code implementations • ACL 2021 • Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L Hamilton, Bryan Catanzaro
We also explore two approaches for end-to-end supervised training of the reader and retriever components in OpenQA models.
no code implementations • ICLR 2019 • Richard Shin, Neel Kant, Kavi Gupta, Christopher Bender, Brandon Trabucco, Rishabh Singh, Dawn Song
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e. g. input-output behavior.
2 code implementations • ICLR 2020 • Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell
Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers.
1 code implementation • 4 Dec 2018 • Neel Kant, Raul Puri, Nikolai Yakovenko, Bryan Catanzaro
Multi-emotion sentiment classification is a natural language processing (NLP) problem with valuable use cases on real-world data.
Ranked #3 on Emotion Classification on SemEval 2018 Task 1E-c (Macro-F1 metric)
1 code implementation • 7 Feb 2018 • 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.