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Found 13 papers, 4 papers with code
Optimizing Multiagent Cooperation via Policy Evolution and Shared Experiences
Training policies solely on the team-based reward is often difficult due to its sparsity.
ProtST: Multi-Modality Learning of Protein Sequences and Biomedical Texts
On downstream tasks, ProtST enables both supervised learning and zero-shot prediction.
Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model
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.
Group SELFIES: A Robust Fragment-Based Molecular String Representation
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
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
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
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.
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization
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.
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
We show that the discovered dense rewards are an effective signal for an RL policy to solve the benchmark tasks.
Safety Aware Reinforcement Learning (SARL)
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.
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning
For deep neural network accelerators, memory movement is both energetically expensive and can bound computation.
Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination
Training policies solely on the team-based reward is often difficult due to its sparsity.
Collaborative Evolutionary Reinforcement Learning
Deep reinforcement learning algorithms have been successfully applied to a range of challenging control tasks.
