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Found 35 papers, 19 papers with code
Sign Rank Limitations for Attention-Based Graph Decoders
Inner product-based decoders are among the most influential frameworks used to extract meaningful data from latent embeddings.
Transformers are efficient hierarchical chemical graph learners
We demonstrate SubFormer on benchmarks for predicting molecular properties from chemical structures and show that it is competitive with state-of-the-art graph transformers at a fraction of the computational cost, with training times on the order of minutes on a consumer-grade graphics card.
P-tensors: a General Formalism for Constructing Higher Order Message Passing Networks
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.
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs
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.
Fast Temporal Wavelet Graph Neural Networks
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.
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
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
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group.
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
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.
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 which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU($d$) symmetries.
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
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.
Symmetry Group Equivariant Architectures for Physics
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.
Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
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.
Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs
Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix factorization algorithms in that it does not make a low rank assumption.
Multiresolution Equivariant Graph Variational Autoencoder
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.
Autobahn: Automorphism-based Graph Neural Nets
Our formalism also encompasses novel architectures: as an example, we introduce a graph neural network that decomposes the graph into paths and cycles.
ATOM3D: Tasks On Molecules in Three Dimensions
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.
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 potential strategies for using data to make scientific predictions.
Ranked #1 on
NMR J-coupling
on QM9
Lorentz Group Equivariant Neural Network for Particle Physics
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 general theory of permutation equivarant neural networks and higher order graph variational encoders
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.
Asymmetric Multiresolution Matrix Factorization
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.
Deep Learning for Automated Classification and Characterization of Amorphous Materials
The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics.
Cormorant: Covariant Molecular Neural Networks
We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems.
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 learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis.
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
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.
N-body Networks: a Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials
We describe N-body networks, a neural network architecture for learning the behavior and properties of complex many body physical systems.
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
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.
Covariant Compositional Networks For Learning Graphs
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.
Multiresolution Kernel Approximation for Gaussian Process Regression
Gaussian process regression generally does not scale to beyond a few thousands data points without applying some sort of kernel approximation method.
The Incremental Multiresolution Matrix Factorization Algorithm
Multiresolution analysis and matrix factorization are foundational tools in computer vision.
The Multiscale Laplacian Graph Kernel
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 #42 on
Graph Classification
on PROTEINS
Parallel MMF: a Multiresolution Approach to Matrix Computation
Multiresolution Matrix Factorization (MMF) was recently introduced as a method for finding multiscale structure and defining wavelets on graphs/matrices.
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
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).
Solving the multi-way matching problem by permutation synchronization
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.
Multiresolution analysis on the symmetric group
There is no generally accepted way to define wavelets on permutations.
On representing chemical environments
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
