Search Results for author: Shizhen Xu

Found 7 papers, 4 papers with code

TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations

1 code implementation9 Oct 2021 Shiyu Huang, Wenze Chen, Longfei Zhang, Shizhen Xu, Ziyang Li, Fengming Zhu, Deheng Ye, Ting Chen, Jun Zhu

To the best of our knowledge, Tikick is the first learning-based AI system that can take over the multi-agent Google Research Football full game, while previous work could either control a single agent or experiment on toy academic scenarios.

Starcraft Starcraft II

LiBRe: A Practical Bayesian Approach to Adversarial Detection

1 code implementation CVPR 2021 Zhijie Deng, Xiao Yang, Shizhen Xu, Hang Su, Jun Zhu

Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.

Adversarial Defense

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

1 code implementation2 Mar 2019 Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang

In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.

Dimensionality Reduction Knowledge Graph Embedding +3

Fast Locality Sensitive Hashing for Beam Search on GPU

no code implementations2 Jun 2018 Xing Shi, Shizhen Xu, Kevin Knight

We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up beam search for sequence models.

Machine Translation Translation

Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks

no code implementations11 Dec 2017 Hao Zhang, Shizhen Xu, Graham Neubig, Wei Dai, Qirong Ho, Guangwen Yang, Eric P. Xing

Recent deep learning (DL) models have moved beyond static network architectures to dynamic ones, handling data where the network structure changes every example, such as sequences of variable lengths, trees, and graphs.

graph construction

Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters

no code implementations11 Jun 2017 Hao Zhang, Zeyu Zheng, Shizhen Xu, Wei Dai, Qirong Ho, Xiaodan Liang, Zhiting Hu, Jinliang Wei, Pengtao Xie, Eric P. Xing

We show that Poseidon enables Caffe and TensorFlow to achieve 15. 5x speed-up on 16 single-GPU machines, even with limited bandwidth (10GbE) and the challenging VGG19-22K network for image classification.

Image Classification

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