47 citations · 180 across the 17 of their papers we have counts for
17 papers
Revisiting Sharpness-Aware Minimization: A More Faithful and Effective Implementation
Jianlong Chen, Zhiming Zhou
Sharpness-Aware Minimization (SAM) enhances generalization by minimizing the maximum training loss within a predefined neighborhood around the parameters. However, its practical im…
Establishing Reliability Metrics for Reward Models in Large Language Models
Yizhou Chen, Yawen Liu, Xuesi Wang +5
The reward model (RM) that represents human preferences plays a crucial role in optimizing the outputs of large language models (LLMs), e.g., through reinforcement learning from hu…
Residual Multi-Task Learner for Applied Ranking
Cong Fu, Kun Wang, Jiahua Wu +5
Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their…
Recurrent Temporal Revision Graph Networks
Yizhou Chen, Anxiang Zeng, Guangda Huzhang +6
Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for tempor…
Clustered Embedding Learning for Recommender Systems
Yizhou Chen, Guangda Huzhang, Anxiang Zeng +7
In recent years, recommender systems have advanced rapidly, where embedding learning for users and items plays a critical role. A standard method learns a unique embedding vector f…
Towards Generalized Implementation of Wasserstein Distance in GANs
Minkai Xu, Zhiming Zhou, Guansong Lu +3
Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it doe…
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Guansong Lu, Zhiming Zhou, Jian Shen +3
Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning. However, existing methods either not explicitly learn the tran…
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Yuxuan Song, Lantao Yu, Zhangjie Cao +5
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. To leverage and adapt…
Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence
Yaoming Zhu, Juncheng Wan, Zhiming Zhou +5
Knowledge base is one of the main forms to represent information in a structured way. A knowledge base typically consists of Resource Description Frameworks (RDF) triples which des…
Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation?
Tianxing He, Jingzhao Zhang, Zhiming Zhou +1
Exposure bias has been regarded as a central problem for auto-regressive language models (LM). It claims that teacher forcing would cause the test-time generation to be incremental…
Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs
Zhiming Zhou, Jian Shen, Yuxuan Song +2
Lipschitz continuity recently becomes popular in generative adversarial networks (GANs). It was observed that the Lipschitz regularized discriminator leads to improved training sta…
Lipschitz Generative Adversarial Nets
Zhiming Zhou, Jiadong Liang, Yuxuan Song +5
In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function…
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport
Guansong Lu, Zhiming Zhou, Yuxuan Song +2
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is…
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods
Zhiming Zhou, Qingru Zhang, Guansong Lu +3
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, bu…
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…
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…