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

最近新闻 / News!

Update: 2024.2.8

视频播放总数已破12万，感谢大家的支持！

第四轮talk信息已发布！

第三轮全部 regular talk 已更新完毕，感谢大家的支持！

2024 R01

Time	Speaker	Talk Title	Talk Info	Paper	Video
Special	李建(清华大学)
03/08	翟润天(CMU)	On the Generalization of Representation Learning and Big Foundation Models	Talk Info	[1, 2]
03/15	罗胜杰(北京大学)	Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products	Talk Info	[1]
03/22	高天宇(Princeton)	Long-Context Language Modeling with Parallel Context Encoding	Talk Info
03/29	邹荻凡(香港大学)	Faster Sampling without Isoperimetry via Diffusion-based Monte Carlo	Talk Info	[1]
04/05	陆一平(NYU)	Simulation-Calibrated Scientific Machine Learning	Talk Info	[1]
04/12	俞鼎力(Princeton)
04/19	吕凯风(Princeton)
04/26	李禹辰(CMU)
05/03	李卓翰(Berkeley)

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]
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