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Found 20 papers, 19 papers with code
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.
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.
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.
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.
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations
We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet.
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.
ViDeBERTa: A powerful pre-trained language model for Vietnamese
This paper presents ViDeBERTa, a new pre-trained monolingual language model for Vietnamese, with three versions - ViDeBERTa_xsmall, ViDeBERTa_base, and ViDeBERTa_large, which are pre-trained on a large-scale corpus of high-quality and diverse Vietnamese texts using DeBERTa architecture.
On the Connection Between MPNN and Graph Transformer
Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple benchmarks.
Ranked #8 on
Node Classification
on PascalVOC-SP
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.
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
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.
Predicting COVID-19 pandemic by spatio-temporal graph neural networks: A New Zealand's study
In this work, we propose a novel deep learning architecture named Attention-based Multiresolution Graph Neural Networks (ATMGNN) that learns to combine the spatial graph information, i. e. geographical data, with the temporal information, i. e. timeseries data of number of COVID-19 cases, to predict the future dynamics of the pandemic.
Neural Multigrid Memory For Computational Fluid Dynamics
Turbulent flow simulation plays a crucial role in various applications, including aircraft and ship design, industrial process optimization, and weather prediction.
Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting
Second, we fit these graph structures and the input data into a Graph Convolutional Recurrent Network (GCRN) to train a forecasting model.
Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learning
To address this issue, we introduce TargetVAE, a target-aware variational auto-encoder that generates ligands with high binding affinities to arbitrary protein targets, guided by a novel multimodal deep neural network built based on graph Transformers as the prior for the generative model.
Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data
This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks.
Symmetry-preserving graph attention network to solve routing problems at multiple resolutions
Travelling Salesperson Problems (TSPs) and Vehicle Routing Problems (VRPs) have achieved reasonable improvement in accuracy and computation time with the adaptation of Machine Learning (ML) methods.
Graph Attention-based Deep Reinforcement Learning for solving the Chinese Postman Problem with Load-dependent costs
We release our C++ implementations for metaheuristics such as EA, ILS and VNS along with the code for data generation and our generated data at https://github. com/HySonLab/Chinese_Postman_Problem
E(3)-Equivariant Mesh Neural Networks
Triangular meshes are widely used to represent three-dimensional objects.
DE-HNN: An effective neural model for Circuit Netlist representation
Using the input and output data of the tools from past designs, one can attempt to build a machine learning model that predicts the outcome of a design in significantly shorter time than running the tool.
