Aston Zhang is a research scientist on the Llama team at Meta Generative AI and a core contributor to Llama 3. Previously, he served as a scientist and manager at AWS AI Research. His accolades include the ICLR Outstanding Paper Award, the ACM Ubicomp Distinguished Paper Award, and an ACM SenSys Best Paper Award nomination. His textbook, “Dive into Deep Learning,” is adopted worldwide. He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign.

News

• Llama 3.1 405B is now openly available. Explore Mark’s insights on Llama 3 and beyond.
• Meet Llama 3, our state-of-the-art open source large language model. Check out my developer podcast.
• Join us for a 2024 research internship (filled, see you next time).

Books

• A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola
  Dive into Deep Learning
  Cambridge University Press, 2023
  
  • Adopted at 500 universities from 70 countries
  • Featured in the AWS re:Invent keynote by Swami, Head of AWS AI, Database, and Analytics
    
• A. Zhang, M. Li, Z. C. Lipton, and A. J. Smola
  动手学深度学习
  人民邮电出版社, 2nd ed., 2023, 1st ed., 2019
  
  • Best seller in China
    

Papers (All)

• Llama Team, AI@Meta (Core Contributor)
  The Llama 3 Herd of Models
  2024
  

• Z. Zhang and A. Zhang
  You Only Look at Screens: Multimodal Chain-of-Action Agents
  “Perform a task on smart phones? Train an agent using screenshots.” In arXiv, 2023
  

• Z. Zhang, A. Zhang, M. Li, H. Zhao, G. Karypis, and A. J. Smola
  Multimodal Chain-of-Thought Reasoning in Language Models
  In Transactions on Machine Learning Research, 2023
  [Idea Inspiration by Homeschooling]

• S. Ren, A. Zhang, Y. Zhu, S. Zhang, S. Zheng, M. Li, A. J. Smola, X. Sun
  Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
  In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2023
  

• Z. Zeng, C. Hawkins, M. Hong, A. Zhang, N. Pappas, V. Singh, and S. Zheng
  Scaling Transformers to 128K Tokens or More by Prioritizing Important Tokens
  In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2023
  

• J. Chen, A. Zhang, X. Shi, M. Li, A. J. Smola, and D. Yang
  Parameter-Efficient Fine-Tuning Design Spaces
  In Proceedings of the International Conference on Learning Representations (ICLR), 2023
  

• Z. Zhang, A. Zhang, M. Li, and A. J. Smola
  Automatic Chain of Thought Prompting in Large Language Models
  In Proceedings of the International Conference on Learning Representations (ICLR), 2023
  

• Z. Liu, Z. Tang, X. Shi, A. Zhang, M. Li, A. Shrivastava, and A. Wilson
  Learning Multimodal Data Augmentation in Feature Space
  In Proceedings of the International Conference on Learning Representations (ICLR), 2023
  

• T. Yang, Y. Zhu, Y. Xie, A. Zhang, C. Chen, and M. Li
  AIM: Adapting Image Models for Efficient Video Understanding
  In Proceedings of the International Conference on Learning Representations (ICLR), 2023
  

• C. Qin, A. Zhang, Z. Zhang, J. Chen, M. Yasunaga, and D. Yang
  Is ChatGPT a General-Purpose Natural Language Processing Task Solver?
  In Empirical Methods in Natural Language Processing (EMNLP), 2023
  

• J. Chen, A. Zhang, D. Yang, M. Li, and A. J. Smola
  A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
  In Empirical Methods in Natural Language Processing (EMNLP), 2023
  

• H. Wang, A. Zhang, Y. Zhu, S. Zheng, M. Li, A. J. Smola, and Z. Wang
  Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition
  In Proceedings of International Conference on Machine Learning (ICML, Long Presentation), 2022
  

• H. Wang, A. Zhang, S. Zheng, X. Shi, M. Li, and Z. Wang
  Removing Batch Normalization Boosts Adversarial Training
  In Proceedings of International Conference on Machine Learning (ICML), 2022
  

• A. Zhang, Y. Tay, S. Zhang, A. Chan, A. T. Luu, S. C. Hui, and J. Fu
  Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with 1/n Parameters
  In Proceedings of the International Conference on Learning Representations (ICLR, Outstanding Paper Award), 2021
  

Tutorials

• with A. J. Smola
  Attention in Deep Learning [Keynote] [PDF] [Video]
  In The 36th International Conference on Machine Learning (ICML), 2019

• with H. Lin, X. Shi, L. Lausen, H. He, S. Zha, and A. J. Smola
  Dive into Deep Learning for Natural Language Processing
  In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019

• with H. Lin, L. Lausen, S. Zha, A. J. Smola, C. Wang, and M. Li
  From Shallow to Deep Language Representations: Pre-training, Fine-tuning, and Beyond [Website]
  In The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019

• with H. Zhang, T. He, Z. Zhang, Z. Zhang, H. Lin, and M. Li
  Everything You Need to Know to Reproduce SOTA Deep Learning Models from Hands-on Tutorial
  In International Conference on Computer Vision (ICCV), 2019

Services

• Area Chair
  • Annual Meeting of the Association for Computational Linguistics (ACL)
  • Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • International Conference on Computational Linguistics (COLING)

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