Search Results for author: Santiago Miret

Found 13 papers, 4 papers with code

Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model

1 code implementation18 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.

Language Modelling Question Answering

Group SELFIES: A Robust Fragment-Based Molecular String Representation

1 code implementation23 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.

PhAST: Physics-Aware, Scalable, and Task-specific GNNs for Accelerated Catalyst Design

no code implementations22 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.

The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science

1 code implementation31 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.

Multi-Objective GFlowNets

no code implementations23 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.

Active Learning Drug Discovery

Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization

no code implementations14 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.


Safety Aware Reinforcement Learning (SARL)

no code implementations6 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.

reinforcement-learning Reinforcement Learning (RL)

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