FAI-Seminar

FAI-Seminar

FAI-Seminar (International Seminar on Foundational Artificial Intelligence) 是一个以人工智能基础为主题的线上中文研讨班。在每一次研讨班中,会有一位讲者分享其近期的工作。欢迎大家来玩!

  • 主题:人工智能基础(以机器学习理论为主,也有有趣的应用工作)

  • 时间:每周五上午10:00 - 11:00 (北京时间)

  • 参加方式:请关注公众号【人工智能基础研究】发送【FAI】加入微信群

  • 官方账号:请关注B站 @FAI-Seminar收看录播/直播,微信公众号

  • 语言:中文

  • 官方手册: 基础信息; 观众须知; 讲者须知


    • 公众号:人工智能基础研究

    最近新闻 / News!


    Update: 2024.6.5

  • 视频播放总数已破17万,感谢大家的支持!

  • 第五轮talk正在筹备中!

  • 第四轮全部 regular talk 已更新完毕,感谢大家的支持!
  • 日程安排 / Schedule


    2024 R02
    Time Speaker Talk Title Talk Info Paper Video
    07/19 张博航
    (北京大学)
    Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness Talk Info [1], [2],
    [3]
    B站
    08/09 黎善达
    (CMU)
    Inference Scaling Law of Large Language Models and Second-Prize Winning Solution of AIMO Talk Info [1], [2] B站
    08/16 王天浩
    (TTIC)
    Tractable training dynamics of transformers for in-context learning Talk Info [1], [2] B站
    08/23 吴京风
    (Berkeley)
    Reimaging Gradient Descent: Large Stepsize, Oscillation, and Acceleration Talk Info [1] B站
    08/30 马梓业
    (港城大)
    Navigating the non-convex landscape via amplifying escape directions of saddle points Talk Info [1], [2],
    [3]
    B站
    11/01 刘勇
    (中国人民大学)
    Can Retrieval Augmented Generation (RAG) Enhance the LLM’s Reasoning Capabilities? Talk Info

    2024 R01
    Time Speaker Talk Title Talk Info Paper Video
    Special talk 05/31 李建
    (清华大学)
    Generalization Error and Implicit Bias of Gradient Methods in Deep Learning Talk Info B站
    03/08 翟润天
    (CMU)
    On the Generalization of Representation Learning and Big Foundation Models Talk Info [1, 2] B站
    03/15 罗胜杰
    (北京大学)
    Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products Talk Info [1] B站
    03/22 高天宇(Princeton) Long-Context Language Modeling with Parallel Context Encoding Talk Info B站
    03/29 邹荻凡
    (香港大学)
    Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo Talk Info [1] B站
    04/05 陆一平
    (NYU)
    Simulation-Calibrated Scientific Machine Learning Talk Info [1] B站
    04/12 俞鼎力(Princeton) Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks Talk Info [1] B站
    04/19 吕凯风(Princeton) Understanding the Limitations of Neural Networks on Algorithmic Reasoning Talk Info [1, 2] B站
    04/26 李禹辰
    (CMU)
    Towards Mathematical Understanding of Modern Language Models Talk Info [1, 2,
    3, 4]
    B站

    2023 R03
    Time Speaker Talk Title Talk Info Paper Video
    Special Talk 2/16 胡威
    (UMich)
    Hidden Structures in Neural Network Representations Talk Info [1, 2] B站
    11/10 陈乐偲
    (清华大学)
    Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles Talk Info [1] B站
    11/17 张博航
    (北京大学)
    Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective Talk Info [1] B站
    11/24 顾欣然
    (清华大学)
    A Quadratic Synchronization Rule for Distributed Deep Learning Talk Info [1] B站
    12/1 石佳欣(DeepMind) MultiresConv: From Wavelet Theory to Long Context Modeling with Neural Networks Talk Info [1] B站
    12/8 范凤磊
    (香港中文
    大学)
    In Pursuit of Deciphering ReLU Networks and Beyond Talk Info [1] B站
    12/15 NeurIPS break
    12/22 刘冰彬
    (CMU)
    Thinking Fast with Transformers: algorithmic reasoning with shortcuts Talk Info [1] (ICLR 23' oral), [2] (NeurIPS 23' spotlight) B站
    12/29 温凯越
    (清华大学)
    Transformers are uninterpretable with myopic methods: a case study with bounded Dyck grammars Talk Info [1] B站
    1/12 游凯超
    (清华大学)
    Understand, Learn, and Adopt the PyTorch compiler (torch.compile) Talk Info [1, 2, 3] B站

    2023 R02
    Time Speaker Talk Title Paper Video
    (Special)09/15 李志远
    (Stanford)
    The Generalization Benefit of Flatnes Regularization [1][2] B站
    06/23 张博航
    (北京大学)
    Understanding the Expressivity of Subgraph-based GNNs for Graph Learning [1] B站
    06/30 罗胜杰
    (北京大学)
    One Transformer Can Understand Both 2D & 3D Molecular Data [1] B站
    07/07 刘子鸣
    (MIT)
    Intelligence from hunger [1], [2] B站
    07/14 马鉴昊
    (UMich)
    Robust Sparse Mean Estimation [1] B站
    07/21 金及凯
    (北京大学)
    Minimax optimal operator learning [1] B站
    07/28 ICML break
    08/04 王博涵
    (中国科学
    技术大学)
    When and Why Momentum Accelerates SGD [1] B站
    08/11 滕佳烨
    (清华大学)
    Predictive inference with feature conformal prediction [1] B站
    08/18 蔡天乐
    (Princeton)
    Large Language Models as Tool Makers [1] B站

    2023 R01
    Time Speaker Talk Title Paper Video
    (Special) 05/26 张景昭
    (清华大学)
    Two Phases of Scaling Laws for Nearest Neighbor Classifiers [1] B站
    03/03 张鼎怀
    (Mila)
    GFlowNets: Exploration for Probabilistic Inference [1],[2],[3],[4] B站
    03/10 顾欣然
    (清华大学)
    Why (and When) does Local SGD Generalize Better than SGD [1] B站
    03/17 王博涵
    (中国科学
    技术大学)
    Provable Benefit of Adaptivity in ADAM [1] B站
    03/24 温凯越
    (清华大学)
    How Does Sharpness-Aware Minimization Minimize Sharpness? [1] B站
    03/31 张博航
    (北京大学)
    Rethinking the Expressive Power of GNNs via Graph Biconnectivity [1] (ICLR 2023 Outstanding Paper) B站
    04/07 马鉴昊(UMich) Escaping Saddle Points Or Not? [1], [2] B站
    04/14 陈乐偲
    (复旦大学)
    On Bilevel Optimization without Lower-level Strong Convexity [1] B站
    04/21 黄凯旋(Princeton) Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data [1] B站
    04/28 戴言
    (清华大学)
    Variance-Aware Sparse Linear Bandits [1] B站

    组织者 / Organizers

    贡献者 / Contributors

    特邀嘉宾 / Invited Guests

    协办单位 / Universities

    合作组织 / Partners

    • AI TIME 论道
      (公众号)
    • 轻松参会
      (公众号)
    • 蔻享学术
      (公众号)