6.2k citations · 7.2k across the 26 of their papers we have counts for
8 papers · 1 filter
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon Wood, Abhishek Das +1
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degree…
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram +4
Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of…
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Anuroop Sriram, Abhishek Das, Brandon M. Wood +2
Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress toward…
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
Weihua Hu, Muhammed Shuaibi, Abhishek Das +5
With massive amounts of atomic simulation data available, there is a huge opportunity to develop fast and accurate machine learning models to approximate expensive physics-based ca…
Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
Vishvak Murahari, Dhruv Batra, Devi Parikh +1
Prior work in visual dialog has focused on training deep neural models on VisDial in isolation. Instead, we present an approach to leverage pretraining on related vision-language d…
Improving Generative Visual Dialog by Answering Diverse Questions
Vishvak Murahari, Prithvijit Chattopadhyay, Dhruv Batra +2
Prior work on training generative Visual Dialog models with reinforcement learning(Das et al.) has explored a Qbot-Abot image-guessing game and shown that this 'self-talk' approach…
IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma +4
We propose a novel framework to identify sub-goals useful for exploration in sequential decision making tasks under partial observability. We utilize the variational intrinsic cont…
TarMAC: Targeted Multi-Agent Communication
Abhishek Das, Théophile Gervet, Joshua Romoff +4
We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing…