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

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.

Graph Generation Self-Supervised 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

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

4 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