568 citations · 6.2k across the 157 of their papers we have counts for
16 papers · 1 filter
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai +3
We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement…
A Neural Stochastic Volatility Model
Rui Luo, Weinan Zhang, Xiaojun Xu +1
In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation o…
GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Hongwei Wang, Jia Wang, Jialin Wang +5
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified i…
Face Transfer with Generative Adversarial Network
Runze Xu, Zhiming Zhou, Weinan Zhang +1
Face transfer animates the facial performances of the character in the target video by a source actor. Traditional methods are typically based on face modeling. We propose an end-t…
Improving Negative Sampling for Word Representation using Self-embedded Features
Long Chen, Fajie Yuan, Joemon M. Jose +1
Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words…
Long Text Generation via Adversarial Training with Leaked Information
Jiaxian Guo, Sidi Lu, Han Cai +3
Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining wit…
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
Yaodong Yang, Lantao Yu, Yiwei Bai +4
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning. Our in…
Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative
Zhiming Zhou, Weinan Zhang, Jun Wang
In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative. --- An advanced…
Efficient Architecture Search by Network Transformation
Han Cai, Tianyao Chen, Weinan Zhang +2
Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their succe…
Learning to Design Games: Strategic Environments in Reinforcement Learning
Haifeng Zhang, Jun Wang, Zhiming Zhou +4
In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its inte…
Wasserstein Distance Guided Representation Learning for Domain Adaptation
Jian Shen, Yanru Qu, Weinan Zhang +1
Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related da…
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
Jun Wang, Lantao Yu, Weinan Zhang +5
This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a quer…
Activation Maximization Generative Adversarial Nets
Zhiming Zhou, Han Cai, Shu Rong +5
Class labels have been empirically shown useful in improving the sample quality of generative adversarial nets (GANs). In this paper, we mathematically study the properties of the…
Unsupervised Diverse Colorization via Generative Adversarial Networks
Yun Cao, Zhiming Zhou, Weinan Zhang +1
Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since ma…
Managing Risk of Bidding in Display Advertising
Haifeng Zhang, Weinan Zhang, Yifei Rong +3
In this paper, we deal with the uncertainty of bidding for display advertising. Similar to the financial market trading, real-time bidding (RTB) based display advertising employs a…
Real-Time Bidding by Reinforcement Learning in Display Advertising
Han Cai, Kan Ren, Weinan Zhang +4
The majority of online display ads are served through real-time bidding (RTB) --- each ad display impression is auctioned off in real-time when it is just being generated from a us…