Zhang, Chiyuan

Understanding deep learning requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 2,838

Understanding deep learning (still) requires rethinking generalization

Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2021) · Communications of the ACM

Stochastic Gradient Optimization TechniquesNeural Networks and Applications

Cited by 2,400

MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., & Zhang, Z. (2015) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 1,819

Do Vision Transformers See Like Convolutional Neural Networks?

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 663

Training Deep Nets with Sublinear Memory Cost

Chen, T., Xu, B., Zhang, C., & Guestrin, C. (2016) · arXiv

Advanced Neural Network ApplicationsStochastic Gradient Optimization Techniques

Cited by 561

Deduplicating Training Data Makes Language Models Better

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 304

Quantifying Memorization Across Neural Language Models

Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C. (2022) · arXiv

Topic ModelingPrivacy-Preserving Technologies in Data

Cited by 204

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Counterfactual Memorization in Neural Language Models

Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., & Carlini, N. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 54

Preventing generation of verbatim memorization in language models gives a false sense of privacy

Ippolito, D., Tramer, F., Nasr, M., Zhang, C., Jagielski, M., Lee, K., Choquette Choo, C., & Carlini, N. (2023) · Proceedings of the 16th International Natural Language Generation Conference

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 28

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards

Huang, Y., Nasr, M., Angelopoulos, A., Carlini, N., Chiang, W.-L., Choquette-Choo, C. A., Ippolito, D., Jagielski, M., Lee, K., Liu, K. Z., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 17

Deduplicating Training Data Makes Language Models Better

Lee, K., Ippolito, D., Nystrom, A., Zhang, C., Eck, D., Callison-Burch, C., & Carlini, N. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Quantifying Cross-Modality Memorization in Vision-Language Models

Wen, Y., Huang, Y., Goldstein, T., Kumar, R., Ghazi, B., & Zhang, C. (2025) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 0