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07/06/2023
Towards Symmetry-Aware Generation of Periodic Materials
We consider the problem of generating periodic materials with deep model...
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06/09/2023
Group Equivariant Fourier Neural Operators for Partial Differential Equations
We consider solving partial differential equations (PDEs) with Fourier n...
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06/08/2023
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in...
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05/25/2023
A Score-Based Model for Learning Neural Wavefunctions
Quantum Monte Carlo coupled with neural network wavefunctions has shown ...
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05/06/2023
A Latent Diffusion Model for Protein Structure Generation
Proteins are complex biomolecules that perform a variety of crucial func...
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04/07/2023
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in m...
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09/23/2022
Periodic Graph Transformers for Crystal Material Property Prediction
We consider representation learning on periodic graphs encoding crystal ...
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07/26/2022
Learning Protein Representations via Complete 3D Graph Networks
We consider representation learning for proteins with 3D structures. We ...
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06/17/2022
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
Many real-world data can be modeled as 3D graphs, but learning represent...
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06/15/2022
Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems
Deep learning methods have been shown to be effective in representing gr...
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02/26/2022
Automated Data Augmentations for Graph Classification
Data augmentations are effective in improving the invariance of learning...
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02/16/2022
Task-Agnostic Graph Explanations
Graph Neural Networks (GNNs) have emerged as powerful tools to encode gr...
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02/16/2022
Self-Supervised Representation Learning via Latent Graph Prediction
Self-supervised learning (SSL) of graph neural networks is emerging as a...
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07/20/2021
Group Contrastive Self-Supervised Learning on Graphs
We study self-supervised learning on graphs using contrastive methods. A...
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04/26/2021
Graph Neural Networks with Adaptive Frequency Response Filter
Graph Neural Networks have recently become a prevailing paradigm for var...
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04/18/2021
Stochastic Optimization of Area Under Precision-Recall Curve for Deep Learning with Provable Convergence
Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common m...
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03/15/2021
Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence
We study text representation methods using deep models. Current methods,...
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03/12/2021
Adversarial Graph Disentanglement
A real-world graph has a complex topology structure, which is often form...
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02/22/2021
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Deep models trained in supervised mode have achieved remarkable success ...
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02/09/2021
On Explainability of Graph Neural Networks via Subgraph Explorations
We consider the problem of explaining the predictions of graph neural ne...
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02/01/2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation
We consider the problem of molecular graph generation using deep models....
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01/14/2021
A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis
Computed tomography (CT) imaging is a promising approach to diagnosing t...
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01/12/2021
CleftNet: Augmented Deep Learning for Synaptic Cleft Detection from Brain Electron Microscopy
Detecting synaptic clefts is a crucial step to investigate the biologica...
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01/06/2021
Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Investigating graph feature learning becomes essentially important with ...
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12/31/2020
Explainability in Graph Neural Networks: A Taxonomic Survey
Deep learning methods are achieving ever-increasing performance on many ...
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11/17/2020
Towards Improved and Interpretable Deep Metric Learning via Attentive Grouping
Grouping has been commonly used in deep metric learning for computing di...
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11/06/2020
Augmented Equivariant Attention Networks for Electron Microscopy Image Super-Resolution
Taking electron microscopy (EM) images in high-resolution is time-consum...
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10/22/2020
Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising
Self-supervised frameworks that learn denoising models with merely indiv...
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10/20/2020
Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples
Predictive modeling is useful but very challenging in biological image a...
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10/20/2020
Line Graph Neural Networks for Link Prediction
We consider the graph link prediction task, which is a classic graph ana...
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10/19/2020
Topology-Aware Graph Pooling Networks
Pooling operations have shown to be effective on computer vision and nat...
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09/15/2020
CorDEL: A Contrastive Deep Learning Approach for Entity Linkage
Entity linkage (EL) is a critical problem in data cleaning and integrati...
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08/05/2020
Global Voxel Transformer Networks for Augmented Microscopy
Advances in deep learning have led to remarkable success in augmented mi...
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07/24/2020
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task...
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07/20/2020
Second-Order Pooling for Graph Neural Networks
Graph neural networks have achieved great success in learning node repre...
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07/18/2020
Deep Learning of High-Order Interactions for Protein Interface Prediction
Protein interactions are important in a broad range of biological proces...
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07/16/2020
Kronecker Attention Networks
Attention operators have been applied on both 1-D data like texts and hi...
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06/03/2020
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Graphs neural networks (GNNs) learn node features by aggregating and com...
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05/29/2020
Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multil...
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07/08/2019
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake new...
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07/05/2019
Graph Representation Learning via Hard and Channel-Wise Attention Networks
Attention operators have been widely applied in various fields, includin...
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07/01/2019
Global Transformer U-Nets for Label-Free Prediction of Fluorescence Images
Visualizing the details of different cellular structures is of great imp...
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05/11/2019
Graph U-Nets
We consider the problem of representation learning for graph data. Convo...
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03/27/2019
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
RNN models have achieved the state-of-the-art performance in a wide rang...
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01/21/2019
Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations
With the development of graph convolutional networks (GCN), deep learnin...
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12/10/2018
Global Deep Learning Methods for Multimodality Isointense Infant Brain Image Segmentation
An important step in early brain development study is to perform automat...
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09/05/2018
ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
Convolutional neural networks (CNNs) have shown great capability of solv...
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08/27/2018
Smoothed Dilated Convolutions for Improved Dense Prediction
Dilated convolutions, also known as atrous convolutions, have been widel...
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08/12/2018
Large-Scale Learnable Graph Convolutional Networks
Convolutional neural networks (CNNs) have achieved great success on grid...
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11/24/2017