Weinan Zhang

GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Molecular graph generation is a fundamental problem for drug discovery a...
01/26/2020 ∙ by Chence Shi, et al. ∙ 20 ∙ share

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Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency

Recent advances in large-scale optimal transport have greatly extended i...
03/14/2020 ∙ by Guansong Lu, et al. ∙ 16 ∙ share

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Signal Instructed Coordination in Team Competition

Most existing models of multi-agent reinforcement learning (MARL) adopt ...
09/10/2019 ∙ by Liheng Chen, et al. ∙ 11 ∙ share

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Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation

With the rapid growth of the express industry, intelligent warehouses th...
11/14/2018 ∙ by Haifeng Zhang, et al. ∙ 6 ∙ share

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Path-Level Network Transformation for Efficient Architecture Search

We introduce a new function-preserving transformation for efficient neur...
06/07/2018 ∙ by Han Cai, et al. ∙ 2 ∙ share

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AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

Learning effective feature interactions is crucial for click-through rat...
03/25/2020 ∙ by Bin Liu, et al. ∙ 2 ∙ share

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GraphGAN: Graph Representation Learning with Generative Adversarial Nets

The goal of graph representation learning is to embed each vertex in a g...
11/22/2017 ∙ by Hongwei Wang, et al. ∙ 0 ∙ share

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Face Transfer with Generative Adversarial Network

Face transfer animates the facial performances of the character in the t...
10/17/2017 ∙ by Runze Xu, et al. ∙ 0 ∙ share

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Efficient Architecture Search by Network Transformation

Techniques for automatically designing deep neural network architectures...
07/16/2017 ∙ by Han Cai, et al. ∙ 0 ∙ share

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Learning to Design Games: Strategic Environments in Deep Reinforcement Learning

In typical reinforcement learning (RL), the environment is assumed given...
07/05/2017 ∙ by Haifeng Zhang, et al. ∙ 0 ∙ share

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Wasserstein Distance Guided Representation Learning for Domain Adaptation

Domain adaptation aims at generalizing a high-performance learner on a t...
07/05/2017 ∙ by Jian Shen, et al. ∙ 0 ∙ share

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Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative

In this paper, we study several GAN related topics mathematically, inclu...
08/05/2017 ∙ by Zhiming Zhou, et al. ∙ 0 ∙ share

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Real-Time Bidding by Reinforcement Learning in Display Advertising

The majority of online display ads are served through real-time bidding ...
01/10/2017 ∙ by Han Cai, et al. ∙ 0 ∙ share

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SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

As a new way of training generative models, Generative Adversarial Nets ...
09/18/2016 ∙ by Lantao Yu, et al. ∙ 0 ∙ share

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Activation Maximization Generative Adversarial Nets

Class label information has been empirically proven to be very useful in...
03/06/2017 ∙ by Zhiming Zhou, et al. ∙ 0 ∙ share

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Unsupervised Diverse Colorization via Generative Adversarial Networks

Colorization of grayscale images has been a hot topic in computer vision...
02/22/2017 ∙ by Yun Cao, et al. ∙ 0 ∙ share

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Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in man...
09/11/2011 ∙ by Tianqi Chen, et al. ∙ 0 ∙ share

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Improving Negative Sampling for Word Representation using Self-embedded Features

Although the word-popularity based negative sampler has shown superb per...
10/26/2017 ∙ by Long Chen, et al. ∙ 0 ∙ share

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Long Text Generation via Adversarial Training with Leaked Information

Automatically generating coherent and semantically meaningful text has m...
09/24/2017 ∙ by Jiaxian Guo, et al. ∙ 0 ∙ share

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Neural Personalized Response Generation as Domain Adaptation

In this paper, we focus on the personalized response generation for conv...
01/09/2017 ∙ by Weinan Zhang, et al. ∙ 0 ∙ share

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Learning to Start for Sequence to Sequence Architecture

The sequence to sequence architecture is widely used in the response gen...
08/19/2016 ∙ by Qingfu Zhu, et al. ∙ 0 ∙ share

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Learning text representation using recurrent convolutional neural network with highway layers

Recently, the rapid development of word embedding and neural networks ha...
06/22/2016 ∙ by Ying Wen, et al. ∙ 0 ∙ share

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Generating and Exploiting Large-scale Pseudo Training Data for Zero Pronoun Resolution

Most existing approaches for zero pronoun resolution are heavily relying...
06/06/2016 ∙ by Ting Liu, et al. ∙ 0 ∙ share

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A Deep Neural Network for Chinese Zero Pronoun Resolution

Existing approaches for Chinese zero pronoun resolution overlook semanti...
04/20/2016 ∙ by Qingyu Yin, et al. ∙ 0 ∙ share

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Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

Real-time bidding (RTB) based display advertising has become one of the ...
03/01/2018 ∙ by Kan Ren, et al. ∙ 0 ∙ share

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Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning

Sponsored search is an indispensable business model and a major revenue ...
03/20/2018 ∙ by Li He, et al. ∙ 0 ∙ share

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Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

Real-time advertising allows advertisers to bid for each impression for ...
02/27/2018 ∙ by Junqi Jin, et al. ∙ 0 ∙ share

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Neural Text Generation: Past, Present and Beyond

This paper presents a systematic survey on recent development of neural ...
03/15/2018 ∙ by Sidi Lu, et al. ∙ 0 ∙ share

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Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

We study the problem of named entity recognition (NER) from electronic m...
04/24/2018 ∙ by Zhenghui Wang, et al. ∙ 0 ∙ share

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CoT: Cooperative Training for Generative Modeling

We propose Cooperative Training (CoT) for training generative models tha...
04/11/2018 ∙ by Sidi Lu, et al. ∙ 0 ∙ share

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An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning

In this paper, we conduct an empirical study on discovering the ordered ...
09/13/2017 ∙ by Yaodong Yang, et al. ∙ 0 ∙ share

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Texygen: A Benchmarking Platform for Text Generation Models

We introduce Texygen, a benchmarking platform to support research on ope...
02/06/2018 ∙ by Yaoming Zhu, et al. ∙ 0 ∙ share

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A Study of AI Population Dynamics with Million-agent Reinforcement Learning

We conduct an empirical study on discovering the ordered collective dyna...
09/13/2017 ∙ by Yaodong Yang, et al. ∙ 0 ∙ share

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A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks

Malicious calls, i.e., telephony spams and scams, have been a long-stand...
04/07/2018 ∙ by Huichen Li, et al. ∙ 0 ∙ share

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Mean Field Multi-Agent Reinforcement Learning

Existing multi-agent reinforcement learning methods are limited typicall...
02/15/2018 ∙ by Yaodong Yang, et al. ∙ 0 ∙ share

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Collaborative Filtering with Graph-based Implicit Feedback

Introducing consumed items as users' implicit feedback in matrix factori...
03/09/2018 ∙ by Minzhe Niu, et al. ∙ 0 ∙ share

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Deep Reinforcement Learning for Chinese Zero pronoun Resolution

Deep neural network models for Chinese zero pronoun resolution learn sem...
06/10/2018 ∙ by Qingyu Yin, et al. ∙ 0 ∙ share

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QA4IE: A Question Answering based Framework for Information Extraction

Information Extraction (IE) refers to automatically extracting structure...
04/10/2018 ∙ by Lin Qiu, et al. ∙ 0 ∙ share

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On the Equilibrium of Query Reformulation and Document Retrieval

In this paper, we study the interactions between query reformulation and...
07/06/2018 ∙ by Shihao Zou, et al. ∙ 0 ∙ share

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AceKG: A Large-scale Knowledge Graph for Academic Data Mining

Most existing knowledge graphs (KGs) in academic domains suffer from pro...
07/23/2018 ∙ by Ruijie Wang, et al. ∙ 0 ∙ share

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Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

In online advertising, the Internet users may be exposed to a sequence o...
08/11/2018 ∙ by Kan Ren, et al. ∙ 0 ∙ share

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Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances

Generative adversarial nets (GANs) have been widely studied during the r...
06/10/2018 ∙ by Weinan Zhang, et al. ∙ 0 ∙ share

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Factorized Q-Learning for Large-Scale Multi-Agent Systems

Deep Q-learning has achieved a significant success in single-agent decis...
09/11/2018 ∙ by Yong Chen, et al. ∙ 0 ∙ share

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MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

We introduce MAgent, a platform to support research and development of m...
12/02/2017 ∙ by Lianmin Zheng, et al. ∙ 0 ∙ share

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Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time...
09/07/2018 ∙ by Kan Ren, et al. ∙ 0 ∙ share

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A Neural Stochastic Volatility Model

In this paper, we show that the recent integration of statistical models...
11/30/2017 ∙ by Rui Luo, et al. ∙ 0 ∙ share

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Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

User response prediction is a crucial component for personalized informa...
07/01/2018 ∙ by Yanru Qu, et al. ∙ 0 ∙ share

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Learning to Advertise with Adaptive Exposure via Constrained Two-Level Reinforcement Learning

For online advertising in e-commerce, the traditional problem is to assi...
09/10/2018 ∙ by Weixun Wang, et al. ∙ 0 ∙ share

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TGE-PS: Text-driven Graph Embedding with Pairs Sampling

In graphs with rich text information, constructing expressive graph repr...
09/12/2018 ∙ by Liheng Chen, et al. ∙ 0 ∙ share

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AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods

Adam is shown not being able to converge to the optimal solution in cert...
09/29/2018 ∙ by Zhiming Zhou, et al. ∙ 0 ∙ share

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Weinan Zhang

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