no code implementations • ICML 2020 • Somdeb Majumdar, Shauharda Khadka, Santiago Miret, Stephen Mcaleer, Kagan Tumer
Training policies solely on the team-based reward is often difficult due to its sparsity.
no code implementations • 28 Jan 2023 • Minghao Xu, Xinyu Yuan, Santiago Miret, Jian Tang
On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
1 code implementation • 18 Dec 2022 • Parishad BehnamGhader, Santiago Miret, Siva Reddy
The emergence of large pretrained models has enabled language models to achieve superior performance in common NLP tasks, including language modeling and question answering, compared to previous static word representation methods.
1 code implementation • 23 Nov 2022 • Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik
We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees.
no code implementations • 22 Nov 2022 • Alexandre Duval, Victor Schmidt, Santiago Miret, Yoshua Bengio, Alex Hernández-García, David Rolnick
Catalyst materials play a crucial role in the electrochemical reactions involved in a great number of industrial processes key to this transition, such as renewable energy storage and electrofuel synthesis.
1 code implementation • 31 Oct 2022 • Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar, Matthew Spellings
We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset.
no code implementations • 23 Oct 2022 • Moksh Jain, Sharath Chandra Raparthy, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Yoshua Bengio, Santiago Miret, Emmanuel Bengio
Through a series of experiments on synthetic and benchmark tasks, we empirically demonstrate that MOGFNs outperform existing methods in terms of Hypervolume, R2-distance and candidate diversity.
no code implementations • 14 Jun 2021 • Santiago Miret, Vui Seng Chua, Mattias Marder, Mariano Phielipp, Nilesh Jain, Somdeb Majumdar
In this work, we present a flexible and scalable framework for automated mixed-precision quantization that concurrently optimizes task performance, memory compression, and compute savings through multi-objective evolutionary computing.
no code implementations • 8 Oct 2020 • Hassam Sheikh, Shauharda Khadka, Santiago Miret, Somdeb Majumdar
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
no code implementations • 6 Oct 2020 • Santiago Miret, Somdeb Majumdar, Carroll Wainwright
Since the safe agent effectively abstracts a task-independent notion of safety via its action probabilities, it can be ported to modulate multiple policies solving different tasks within the given environment without further training.
no code implementations • ICLR 2021 • Shauharda Khadka, Estelle Aflalo, Mattias Marder, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
no code implementations • 18 Jun 2019 • Shauharda Khadka, Somdeb Majumdar, Santiago Miret, Stephen Mcaleer, Kagan Tumer
Training policies solely on the team-based reward is often difficult due to its sparsity.
1 code implementation • 2 May 2019 • Shauharda Khadka, Somdeb Majumdar, Tarek Nassar, Zach Dwiel, Evren Tumer, Santiago Miret, Yinyin Liu, Kagan Tumer
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks.