3.2k citations · 6.9k across the 41 of their papers we have counts for
43 papers
Deep Minds and Shallow Probes
Su Hyeong Lee, Risi Kondor
Neural representations are not unique objects. Even when two systems realize the same downstream computation, their hidden coordinates may differ by reparameterization. A probe fam…
A Geometric Approach to Steerable Convolutions
Soumyabrata Kundu, Risi Kondor
In contrast to the somewhat abstract, group theoretical approach adopted by many papers, our work provides a new and more intuitive derivation of steerable convolutional neural net…
Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
Su Hyeong Lee, Risi Kondor, Richard Ngo
We develop a theory of intelligent agency grounded in probabilistic modeling for neural models. Agents are represented as outcome distributions with epistemic utility given by log…
Graph Contrastive Learning versus Untrained Baselines: The Role of Dataset Size
Smayan Khanna, Doruk Efe Gökmen, Risi Kondor +1
Graph Contrastive Learning (GCL) has emerged as a leading paradigm for self-supervised learning on graphs, with strong performance reported on standardized datasets and growing app…
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
Truong Son Hy, Thieu Khang, Risi Kondor
Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix factorization algorithms in that it does not make a low rank assumption. This makes MMF especially well su…
Steerable Transformers for Volumetric Data
Soumyabrata Kundu, Risi Kondor
We introduce Steerable Transformers, an extension of the Vision Transformer mechanism that maintains equivariance to the special Euclidean group . We propose an equ…
Sign Rank Limitations for Inner Product Graph Decoders
Su Hyeong Lee, Qingqi Zhang, Risi Kondor
Inner product-based decoders are among the most influential frameworks used to extract meaningful data from latent embeddings. However, such decoders have shown limitations in repr…
Transformers are efficient hierarchical chemical graph learners
Zihan Pengmei, Zimu Li, Chih-chan Tien +2
Transformers, adapted from natural language processing, are emerging as a leading approach for graph representation learning. Contemporary graph transformers often treat nodes or e…
P-Tensors: a General Formalism for Constructing Higher Order Message Passing Networks
Andrew Hands, Tianyi Sun, Risi Kondor
Several recent papers have proposed increasing the expressive power of graph neural networks by exploiting subgraphs or other topological structures. In parallel, researchers have…
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large n…
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to det…
Fast Temporal Wavelet Graph Neural Networks
Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy +1
Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate…
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Zimu Li, Zihan Pengmei, Han Zheng +3
Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general…
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on l…
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU() Symmetry
Han Zheng, Zimu Li, Sergii Strelchuk +2
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU sym…
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh +1
In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph st…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…
Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
Han Zheng, Zimu Li, Junyu Liu +2
We develop a theoretical framework for -equivariant convolutional quantum circuits with SU-symmetry, building on and significantly generalizing Jordan's Permutational Qua…
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
Truong Son Hy, Risi Kondor
Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix factorization algorithms in that it does not make a low rank assumption. This makes MMF especially well su…
Multiresolution Equivariant Graph Variational Autoencoder
Truong Son Hy, Risi Kondor
In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolut…
Autobahn: Automorphism-based Graph Neural Nets
Erik Henning Thiede, Wenda Zhou, Risi Kondor
We introduce Automorphism-based graph neural networks (Autobahn), a new family of graph neural networks. In an Autobahn, we decompose the graph into a collection of subgraphs and a…
ATOM3D: Tasks On Molecules in Three Dimensions
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana +10
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural net…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann +3
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The…