Search Results for author: Zhiming Zhou

Found 18 papers, 8 papers with code

Learning to Branch in Combinatorial Optimization with Graph Pointer Networks

no code implementations4 Jul 2023 Rui Wang, Zhiming Zhou, Tao Zhang, Ling Wang, Xin Xu, Xiangke Liao, Kaiwen Li

The proposed model, which combines the graph neural network and the pointer mechanism, can effectively map from the solver state to the branching variable decisions.

Combinatorial Optimization Variable Selection

Clustered Embedding Learning for Recommender Systems

no code implementations3 Feb 2023 Yizhou Chen, Guangda Huzhang, AnXiang Zeng, Qingtao Yu, Hui Sun, Heng-yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou

However, such a method has two important limitations in real-world applications: 1) it is hard to learn embeddings that generalize well for users and items with rare interactions on their own; and 2) it may incur unbearably high memory costs when the number of users and items scales up.

Recommendation Systems

Towards Generalized Implementation of Wasserstein Distance in GANs

1 code implementation7 Dec 2020 Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang, Weinan Zhang, Yong Yu

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models.

Quantifying Exposure Bias for Open-ended Language Generation

no code implementations28 Sep 2020 Tianxing He, Jingzhao Zhang, Zhiming Zhou, James R. Glass

The exposure bias problem refers to the incrementally distorted generation induced by the training-generation discrepancy, in teacher-forcing training for auto-regressive neural network language models (LM).

Text Generation

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck

no code implementations21 Nov 2019 Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou, Jian Shen, Shuo Shao, Wei-Nan Zhang, Yong Yu

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.

Unsupervised Domain Adaptation

Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs

1 code implementation2 Apr 2019 Zhiming Zhou, Jian Shen, Yuxuan Song, Wei-Nan Zhang, Yong Yu

Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).

Lipschitz Generative Adversarial Nets

1 code implementation15 Feb 2019 Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Wei-Nan Zhang, Yong Yu, Zhihua Zhang

By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem.

Informativeness

Guiding the One-to-one Mapping in CycleGAN via Optimal Transport

no code implementations15 Nov 2018 Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation.

Translation

Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets

1 code implementation2 Jul 2018 Zhiming Zhou, Yuxuan Song, Lantao Yu, Hongwei Wang, Jiadong Liang, Wei-Nan Zhang, Zhihua Zhang, Yong Yu

In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs.

valid

Face Transfer with Generative Adversarial Network

no code implementations17 Oct 2017 Runze Xu, Zhiming Zhou, Wei-Nan Zhang, Yong Yu

Face transfer animates the facial performances of the character in the target video by a source actor.

Face Transfer Generative Adversarial Network

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

no code implementations5 Aug 2017 Zhiming Zhou, Wei-Nan 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.

Learning to Design Games: Strategic Environments in Reinforcement Learning

no code implementations5 Jul 2017 Haifeng Zhang, Jun Wang, Zhiming Zhou, Wei-Nan Zhang, Ying Wen, Yong Yu, Wenxin Li

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 interactions with the environment.

Game Design reinforcement-learning +1

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