Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.

Out-of-Distribution Generalization Transfer Learning

Group Equivariant Fourier Neural Operators for Partial Differential Equations

1 code implementation • 9 Jun 2023 • Jacob Helwig, Xuan Zhang, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, Shuiwang Ji

We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain.

A Latent Diffusion Model for Protein Structure Generation

no code implementations • 6 May 2023 • Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji

Proteins are complex biomolecules that perform a variety of crucial functions within living organisms.

Drug Discovery

Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems

no code implementations • 15 Jun 2022 • Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, Shuiwang Ji

Based on the proposed lattice convolutions, we design lattice convolutional networks (LCN) that use self-gating and attention mechanisms.

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks

1 code implementation • NeurIPS Workshop AI4Scien 2021 • Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji

We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.

Molecular Property Prediction Property Prediction

DIG: A Turnkey Library for Diving into Graph Deep Learning Research

1 code implementation • 23 Mar 2021 • Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji

Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.

Benchmarking Graph Generation +1

Cross-Domain Recommendation via Preference Propagation GraphNet

no code implementations • Conference 2019 • Cheng Zhao, Chenliang Li, Cong Fu

We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure.

Link Prediction Transfer Learning

Collaborative Policy Learning for Open Knowledge Graph Reasoning

2 code implementations • IJCNLP 2019 • Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren

We propose a novel reinforcement learning framework to train two collaborative agents jointly, i. e., a multi-hop graph reasoner and a fact extractor.

High Dimensional Similarity Search with Satellite System Graph: Efficiency, Scalability, and Unindexed Query Compatibility

2 code implementations • 13 Jul 2019 • Cong Fu, Changxu Wang, Deng Cai

However, we find there are several limitations with NSG: 1) NSG has no theoretical guarantee on nearest neighbor search when the query is not indexed in the database; 2) NSG is too sparse which harms the search performance.

Information Retrieval Retrieval

COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning

1 code implementation • 25 Jun 2019 • Wenxiao Wang, Cong Fu, Jishun Guo, Deng Cai, Xiaofei He

2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer.

Neural Network Compression

Fast Approximate Nearest Neighbor Search With The Navigating Spreading-out Graph

2 code implementations • 1 Jul 2017 • Cong Fu, Chao Xiang, Changxu Wang, Deng Cai

In this paper, to further improve the search-efficiency and scalability of graph-based methods, we start by introducing four aspects: (1) ensuring the connectivity of the graph; (2) lowering the average out-degree of the graph for fast traversal; (3) shortening the search path; and (4) reducing the index size.

EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph

5 code implementations • 23 Sep 2016 • Cong Fu, Deng Cai

In this paper, we propose EFANNA, an extremely fast approximate nearest neighbor search algorithm based on $k$NN Graph.

graph construction