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The Data Integration, Visualization, and Exploration (DIVE) Laboratory at Texas A&M University is led by Dr. Shuiwang Ji and conducts foundational research in machine learning and deep learning and applies machine learning methods to solve challenging real-world problems in biology, chemistry, neuroscience and medicine.

Highlighted Work

DIVE Lab, Texas A&M University's Projects

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Artificial Intelligence Research for Science (AIRS)

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Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [ICLR 2024]

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Website for NSF project: III: Small: Deep Learning for Expression Pattern Image Analysis

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Computational Modeling of Cellular Structures Using Conditional Deep Generative Networks

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Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

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A library for graph deep learning research

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Smoothed Dilated Convolutions for Improved Dense Prediction

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OOD Dataset Curator and Benchmark for AI-aided Drug Discovery

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GOOD: A Graph Out-of-Distribution Benchmark [NeurIPS 2022 Datasets and Benchmarks]

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Official implementation of "Generating 3D Molecules for Target Protein Binding" [ICML2022 Long Presentation]

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Pytorch implementation of Graph U-Nets (ICML19)

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Global Voxel Transformer Networks (GVTNets)

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TensorFlow implementation of Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations (WWW19)

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The implementation of "Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization" (NeurIPS 2023)

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