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 • 7 Aug 2023 • Ahmed Agiza, Rajarshi Roy, Teodor Dumitru Ene, Saad Godil, Sherief Reda, Bryan Catanzaro
Given a gate-level netlist of a circuit represented as a graph, GraPhSyM utilizes graph structure, connectivity, and electrical property features to predict the impact of physical synthesis transformations such as buffer insertion and gate sizing.
3 code implementations • NeurIPS 2023 • Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar
Using this data, we develop ReProver (Retrieval-Augmented Prover): an LLM-based prover augmented with retrieval for selecting premises from a vast math library.
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.
no code implementations • 6 Sep 2021 • Robert Kirby, Kolby Nottingham, Rajarshi Roy, Saad Godil, Bryan Catanzaro
In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL).
1 code implementation • NeurIPS 2020 • Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro
While more work is needed to apply Graph-Q-SAT to reduce wall clock time in modern SAT solving settings, it is a compelling proof-of-concept showing that RL equipped with Graph Neural Networks can learn a generalizable branching heuristic for SAT search.
2 code implementations • 26 Sep 2019 • Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro
While more work is needed to apply Graph-$Q$-SAT to reduce wall clock time in modern SAT solving settings, it is a compelling proof-of-concept showing that RL equipped with Graph Neural Networks can learn a generalizable branching heuristic for SAT search.
no code implementations • 25 Sep 2019 • Vitaly Kurin, Saad Godil, Shimon Whiteson, Bryan Catanzaro
We present GQSAT, a branching heuristic in a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation.