报告时间：05月31日（周三）15:00-16:30

报告地点：电信群楼4号楼E谷

报告人：张昊 

加州大学圣迭戈分校助理教授

主持人：邓志杰 

上海交通大学清源研究院助理教授

摘要:While deep learning achieves great success in many applications, there is still lack of theoretical understandings. In this talk I will present our recent works on the theories of the representation power, optimization and generalization of deep learning. I first show deep neural networks with bounded width are universal approximators. Then I will talk about the training of a deep neural network. Traditional wisdom says that training deep nets is a highly nonconvex optimization problem. However, empirically one can often find global minima simply using gradient descent. I show that if the deep net is sufficiently wide, then starting from a random initialization, gradient descent provably finds global optima with a linear convergence rate. Finally, I will talk about why overparameterized deep neural networks can have good generalization.

简介：Hao Zhang is an Assistant Professor at Halıcıoğlu Data Science Institute and the Department of Computer Science and Engineering at UCSD. His research interests are in the intersection of machine learning and systems, focusing on improving the performance and ease-of-use of today’s distributed ML systems. Recently, Hao has been working actively on democratizing access to large language models (LLMs). Hao has created several popular open-source LLM projects, such as Alpa, Vicuna, and Fastchat. Hao’s research has been recognized with an NVIDIA pioneer research award at NeurIPS’17, and the Jay Lepreau best paper award at OSDI’21.  Hao's previous open-source artifacts in ML systems have been used by organizations such as AI2, Meta, and Google. Parts of Hao's research have been commercialized at multiple start-ups including Petuum and AnyScale。

上海交通大学清源研究院成立于2019年12月20日，

致力于构建世界一流的人工智能科研与教学队伍，

专注于人工智能的基础理论研究与技术创新，

以期取得具有国际领先水平的创新成果，

推动大学与产业的有机融合，

为人工智能的理论研究及产业发展作出贡献。

本系列讲座长期进行，更多发现敬请关注公号。