Risi Kondor | DeepAI

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∙ 06/19/2023

P-tensors: a General Formalism for Constructing Higher Order Message Passing Networks

Several recent papers have recently shown that higher order graph neural...

0 Tianyi Sun, et al. ∙

∙ 02/17/2023

Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs

Latent representations of drugs and their targets produced by contempora...

0 Nhat-Khang Ngo, et al. ∙

∙ 02/17/2023

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures

Contemporary graph learning algorithms are not well-defined for large mo...

0 Nhat-Khang Ngo, et al. ∙

∙ 02/17/2023

Fast Temporal Wavelet Graph Neural Networks

Spatio-temporal signals forecasting plays an important role in numerous ...

0 Duc Thien Nguyen, et al. ∙

∙ 11/14/2022

Group-Equivariant Neural Networks with Fusion Diagrams

Many learning tasks in physics and chemistry involve global spatial symm...

0 Zimu Li, et al. ∙

∙ 07/15/2022

On the Super-exponential Quantum Speedup of Equivariant Quantum Machine Learning Algorithms with SU(d) Symmetry

We introduce a framework of the equivariant convolutional algorithms whi...

0 Han Zheng, et al. ∙

∙ 05/31/2022

Learning to Represent Programs with Code Hierarchies

Graph neural networks have been shown to produce impressive results for ...

0 Minh H. Nguyen, et al. ∙

∙ 05/30/2022

Temporal Multiresolution Graph Neural Networks For Epidemic Prediction

In this paper, we introduce Temporal Multiresolution Graph Neural Networ...

0 Truong-Son Hy, et al. ∙

∙ 12/14/2021

Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze

We develop a theoretical framework for S_n-equivariant quantum convoluti...

0 Han Zheng, et al. ∙

∙ 11/02/2021

Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs

Multiresolution Matrix Factorization (MMF) is unusual amongst fast matri...

0 Truong-Son Hy, et al. ∙

∙ 06/02/2021

Multiresolution Graph Variational Autoencoder

In this paper, we propose Multiresolution Graph Networks (MGN) and Multi...

2 Truong-Son Hy, et al. ∙

∙ 03/02/2021

Autobahn: Automorphism-based Graph Neural Nets

We introduce Automorphism-based graph neural networks (Autobahn), a new ...

0 Erik Henning Thiede, et al. ∙

∙ 12/07/2020

ATOM3D: Tasks On Molecules in Three Dimensions

Computational methods that operate directly on three-dimensional molecul...

0 Raphael J. L. Townshend, et al. ∙

∙ 08/13/2020

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potent...

0 Lars A. Bratholm, et al. ∙

∙ 06/08/2020

Lorentz Group Equivariant Neural Network for Particle Physics

We present a neural network architecture that is fully equivariant with ...

7 Alexander Bogatskiy, et al. ∙

∙ 04/08/2020

The general theory of permutation equivarant neural networks and higher order graph variational encoders

Previous work on symmetric group equivariant neural networks generally o...

0 Erik Henning Thiede, et al. ∙

∙ 10/10/2019

Asymmetric Multiresolution Matrix Factorization

Multiresolution Matrix Factorization (MMF) was recently introduced as an...

0 Pramod Kaushik Mudrakarta, et al. ∙

∙ 09/10/2019

Deep Learning for Automated Classification and Characterization of Amorphous Materials

It is difficult to quantify structure-property relationships and to iden...

6 Kirk Swanson, et al. ∙

∙ 06/06/2019

Cormorant: Covariant Molecular Neural Networks

We propose Cormorant, a rotationally covariant neural network architectu...

0 Brandon Anderson, et al. ∙

∙ 06/24/2018

Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network

Recent work by Cohen et al. has achieved state-of-the-art results for le...

0 Risi Kondor, et al. ∙

∙ 03/05/2018

N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials

We describe N-body networks, a neural network architecture for learning ...

0 Risi Kondor, et al. ∙

∙ 02/11/2018

On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups

Convolutional neural networks have been extremely successful in the imag...

0 Risi Kondor, et al. ∙

∙ 01/07/2018

Covariant Compositional Networks For Learning Graphs

Most existing neural networks for learning graphs address permutation in...

0 Risi Kondor, et al. ∙

∙ 08/07/2017

Multiresolution Kernel Approximation for Gaussian Process Regression

Gaussian process regression generally does not scale to beyond a few tho...

0 Yi Ding, et al. ∙

∙ 05/16/2017

The Incremental Multiresolution Matrix Factorization Algorithm

Multiresolution analysis and matrix factorization are foundational tools...

0 Vamsi K. Ithapu, et al. ∙

∙ 03/20/2016

The Multiscale Laplacian Graph Kernel

Many real world graphs, such as the graphs of molecules, exhibit structu...

0 Risi Kondor, et al. ∙

∙ 06/27/2012

Incorporating Domain Knowledge in Matching Problems via Harmonic Analysis

Matching one set of objects to another is a ubiquitous task in machine l...

0 Deepti Pachauri, et al. ∙

∙ 01/20/2007

A novel set of rotationally and translationally invariant features for images based on the non-commutative bispectrum

We propose a new set of rotationally and translationally invariant featu...

0 Risi Kondor, et al. ∙

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