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no code implementations • 19 Jun 2023 • Tianyi Sun, Andrew Hands, Risi Kondor

Several recent papers have recently shown that higher order graph neural networks can achieve better accuracy than their standard message passing counterparts, especially on highly structured graphs such as molecules.

1 code implementation • 17 Feb 2023 • Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy, Risi Kondor

To facilitate reliable and timely forecast for the human brain and traffic networks, we propose the Fast Temporal Wavelet Graph Neural Networks (FTWGNN) that is both time- and memory-efficient for learning tasks on timeseries data with the underlying graph structure, thanks to the theories of multiresolution analysis and wavelet theory on discrete spaces.

2 code implementations • 17 Feb 2023 • 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 determine the molecular properties of macromolecules.

Ranked #2 on Graph Regression on Peptides-struct

1 code implementation • 17 Feb 2023 • 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 networks, including drug-drug, drug-target, and target-target interactions.

no code implementations • 14 Nov 2022 • Zimu Li, Han Zheng, Erik Thiede, Junyu Liu, Risi Kondor

However, as the number of different tensors and the complexity of relationships between them increases, the bookkeeping associated with ensuring parsimony as well as equivariance quickly becomes nontrivial.

1 code implementation • 14 Sep 2022 • Nhat Khang Ngo, Truong Son Hy, Risi Kondor

However, most existing approaches model the node's latent spaces in which node distributions are rigid and disjoint; these limitations hinder the methods from generating new links among pairs of nodes.

no code implementations • 15 Jul 2022 • Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, Risi Kondor

We introduce a framework of the equivariant convolutional algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU($d$) symmetries.

1 code implementation • 30 May 2022 • Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh, Risi Kondor

In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph structures and incorporates the time-series signals to capture the temporal changes of the dynamic graphs.

no code implementations • 11 Mar 2022 • Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais

Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.

1 code implementation • 14 Dec 2021 • Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, Risi Kondor

We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits.

1 code implementation • 2 Nov 2021 • 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.

2 code implementations • 2 Jun 2021 • 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 multiresolution and equivariant manner.

1 code implementation • NeurIPS 2021 • Erik Henning Thiede, Wenda Zhou, Risi Kondor

Our formalism also encompasses novel architectures: as an example, we introduce a graph neural network that decomposes the graph into paths and cycles.

3 code implementations • 7 Dec 2020 • Raphael J. L. Townshend, Martin Vögele, Patricia Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon Anderson, Stephan Eismann, Risi Kondor, Russ B. Altman, Ron O. Dror

We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.

1 code implementation • 13 Aug 2020 • Lars A. Bratholm, Will Gerrard, Brandon Anderson, Shaojie Bai, Sunghwan Choi, Lam Dang, Pavel Hanchar, Addison Howard, Guillaume Huard, Sanghoon Kim, Zico Kolter, Risi Kondor, Mordechai Kornbluth, Youhan Lee, Youngsoo Lee, Jonathan P. Mailoa, Thanh Tu Nguyen, Milos Popovic, Goran Rakocevic, Walter Reade, Wonho Song, Luka Stojanovic, Erik H. Thiede, Nebojsa Tijanic, Andres Torrubia, Devin Willmott, Craig P. Butts, David R. Glowacki, Kaggle participants

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions.

Ranked #1 on NMR J-coupling on QM9

3 code implementations • ICML 2020 • Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, Risi Kondor

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.

1 code implementation • 8 Apr 2020 • Erik Henning Thiede, Truong Son Hy, Risi Kondor

Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector.

no code implementations • 10 Oct 2019 • Pramod Kaushik Mudrakarta, Shubhendu Trivedi, Risi Kondor

Multiresolution Matrix Factorization (MMF) was recently introduced as an alternative to the dominant low-rank paradigm in order to capture structure in matrices at multiple different scales.

no code implementations • 10 Sep 2019 • Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi Kondor

The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics.

2 code implementations • NeurIPS 2019 • Brandon Anderson, Truong-Son Hy, Risi Kondor

We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems.

no code implementations • NeurIPS 2018 • Risi Kondor, Zhen Lin, Shubhendu Trivedi

Recent work by Cohen et al. has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis.

1 code implementation • 24 Jun 2018 • Risi Kondor, Zhen Lin, Shubhendu Trivedi

Recent work by Cohen \emph{et al.} has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis.

no code implementations • 5 Mar 2018 • Risi Kondor

We describe N-body networks, a neural network architecture for learning the behavior and properties of complex many body physical systems.

no code implementations • ICML 2018 • Risi Kondor, Shubhendu Trivedi

In this paper we give a rigorous, theoretical treatment of convolution and equivariance in neural networks with respect to not just translations, but the action of any compact group.

2 code implementations • ICLR 2018 • Risi Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi

Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors.

1 code implementation • NeurIPS 2017 • Yi Ding, Risi Kondor, Jonathan Eskreis-Winkler

Gaussian process regression generally does not scale to beyond a few thousands data points without applying some sort of kernel approximation method.

no code implementations • CVPR 2017 • Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh

Multiresolution analysis and matrix factorization are foundational tools in computer vision.

no code implementations • NeurIPS 2016 • Risi Kondor, Horace Pan

At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs.

Ranked #41 on Graph Classification on PROTEINS

no code implementations • 15 Jul 2015 • Risi Kondor, Nedelina Teneva, Pramod K. Mudrakarta

Multiresolution Matrix Factorization (MMF) was recently introduced as a method for finding multiscale structure and defining wavelets on graphs/matrices.

no code implementations • NeurIPS 2014 • Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh

Consistently matching keypoints across images, and the related problem of finding clusters of nearby images, are critical components of various tasks in Computer Vision, including Structure from Motion (SfM).

no code implementations • NeurIPS 2013 • Deepti Pachauri, Risi Kondor, Vikas Singh

The problem of matching not just two, but m different sets of objects to each other arises in a variety of contexts, including finding the correspondence between feature points across multiple images in computer vision.

no code implementations • NeurIPS 2012 • Risi Kondor, Walter Dempsey

There is no generally accepted way to define wavelets on permutations.

2 code implementations • 14 Sep 2012 • Albert P. Bartók, Risi Kondor, Gábor Csányi

We review some recently published methods to represent atomic neighbourhood environments, and analyse their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces.

Computational Physics Materials Science

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