Search Results for author:
Found 7 papers, 4 papers with code
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
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.
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
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.
Ranked #1 on
Node Classification
on YouTube
Fast Locality Sensitive Hashing for Beam Search on GPU
We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up beam search for sequence models.
Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
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.
Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designated semantics or structures.
Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
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.
