Search Results for author: Cong Fu

Found 12 papers, 9 papers with code

Group Equivariant Fourier Neural Operators for Partial Differential Equations

1 code implementation9 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.

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

no code implementations15 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.

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

1 code implementation23 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 implementations13 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 implementation25 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 implementations1 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 implementations23 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

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