演讲人:Yian Ma (马易安) [University of California, San Diego]时间:13:30-15:00, Dec 26, 2025 (Fri)地点:RM 1-222, FIT Building内容:In the first part of the talk, I will discuss an interesting phenomenon in multiagent learning, that the mixed Nash equilibria are uniformly stable if and only if they are collectively rational. This justifies the effusive use of multi-agent learning systems and r...
演讲人:龚明 [中国科学技术大学微尺度国家研究中心] 时间:15:00-17:30, Dec 23, 2025 (Tue)地点:RM S327, MMW Building内容:超导量子计算技术发展的核心目标是通过提升量子比特数目及操控质量,实现通用容错量子计算机。其实现路径上有三个重要的里程碑,分别是量子计算优越性、专用量子模拟、以及通用量子计算。本团队在2021年实现了量子优越性的里程碑,展示了超越经典计算的能力,为探索量子增强的近期应用提供了更多机...
演讲人:Yunbei Xu [NUS]时间:11:00-12:00, Dec 23, 2025 (Tue)地点:RM 1-222, FIT Building内容:We address the fundamental question of why deep neural networks generalize by establishing a pointwise generalization theory for fully connected networks. This framework resolves long-standing barriers to characterizing the rich, nonlinear feature-learning regime and builds a new statistical foundation...
演讲人:张钧翔 时间:13:30-16:00, Dec 19, 2025 (Fri)地点:RM S327, MMW Building内容:Nuclear spins ubiquitously present in diamond nitrogen-vacancy (NV) center systems exhibit exceptional properties as quantum resources, including long coherence times, efficient polarization, and versatile readout modalities. This report systematically investigates quantum control and measurement methodologies ...
演讲人:贺笛 [北京大学] 时间:12:00-13:00, Dec 16, 2025 (Tue)地点:FIT 1-312内容:随着大规模视觉生成模型的迅速扩展,内容来源可追溯性与模型滥用检测成为影响生成式 AI 健康发展的关键问题。模型级水印通过在生成过程中内嵌可识别信号,为判断图像是否由特定模型生成提供了必要的技术基础。然而,传统学习式水印依赖黑箱神经网络,其检测结果缺乏严格的可靠性保证,难以量化错误率。相比之下,具备统计可证性的水印方法...
演讲人:李绿周 [中山大学]时间:11:00-12:00, Dec 11, 2025 (Thu)地点:MMW S727内容:量子计算的有用性有赖于量子算法。量子算法的设计具有挑战性,需要在量子视角下找到有利于量子特性发挥作用的结构信息,从而到达返璞归真,大道至简的效果。基于量子计算领域现状,除了要在量子计算硬件方面做出长足努力,也需要进一步发展量子算法基础理论,拓展量子计算能力边界。本报告以量子算法为核心,汇报团队近年来在量子算法设计...