最优传输理论课程信息：
北京时间星期六10月17日晚9:00-10:30pm，北京时间星期日10月18日晩上9:00-10:30pm，继续讲解最优传输课程，内容为Minkowksi、Alexandrov定理，包括Convex Hull算法，Brunn-Minkowski不等式，不动点定理，domain invariance定理，Alexandrov定理，Pogorelov定理证明等。
布置第一次作业：3维凸包算法
欢迎参加：
zoom ID：578 945 9691
B站：online.conformalgeometry.org
@所有人
北京时间星期日，10/18/2020早晨，9:00-9:50，The 16th Annual Conference on Theory and Applications of Models of Computation，
Keynote Speech： A Geometric Understanding of Deep Learning By Xianfeng Gu
Everyone is welcomed to join.
The conference website:
https://tamc2020.csu.edu.cn/html/Program.html
最优传输理论和计算共形几何过往课程在B站上可以找到：
https://space.bilibili.com/446605493/
昨天哈佛的演讲可以在腾讯视频上看到：
介绍来自：
https://cmsa.fas.harvard.edu/literature-lecture-series/
内容来自：
https://www.youtube.com/watch?v=Olmsu6pg0lM&feature=push-sd&attr_tag=3czPynnwdwfl5VIn%3A6
Harry Shum (Tsinghua University)
Title: From Deep Learning to Deep Understanding
Abstract: In this talk I will discuss a couple of research directions for robust AI beyond deep neural networks. The first is the need to understand what we are learning, by shifting the focus from targeting effects to understanding causes. The second is the need for a hybrid neural/symbolic approach that leverages both commonsense knowledge and massive amount of data. Specifically, as an example, I will present some latest work at Microsoft Research on building a pre-trained grounded text generator for task-oriented dialog. It is a hybrid architecture that employs a large-scale Transformer-based deep learning model, and symbol manipulation modules such as business databases, knowledge graphs and commonsense rules. Unlike GPT or similar language models learnt from data, it is a multi-turn decision making system which takes user input, updates the belief state, retrieved from the database via symbolic reasoning, and decides how to complete the task with grounded response.
本文转载自久章智能微信公众号（原文发布于 2020-10-16），内容保持原貌。扫码关注公众号「久章智能」，第一时间获取系列文章。阅读原文 ↗