Events for 10/16/2020 from all calendars

SIAM Texas-Louisiana Section conference

URL: Event link

Student Working Seminar in Groups and Dynamics

Time: 11:00AM - 12:00PM

Location: Zoom

Speaker: Alex Weygandt

Title: Topological Dynamics and Operator Algebras

Abstract: In topological dynamics, one studies (groups of) homeomorphisms on topological spaces. Under mild assumptions, one can generate C*-algebras, called transformation group C^*-algebras, which capture many of the properties of such dynamical systems. In this talk, I will define how one obtains transformation group C*-algebras, and discuss how properties of topological dynamical systems induce properties of the corresponding transformation group C*-algebra, and vice versa.

Spectral Theory Reading Seminar

Time: 11:00AM - 11:50AM

Location: Zoom

Speaker: Wencai Liu, Texas A&M University

Title: Several approaches to calculate the spectrum of the free discrete Schrodinger operator I

Open Teaching with Technology Forum

Time: 3:30PM - 5:00PM

Location: Zoom

Speaker: Vanessa Coffelt

Description: Join Zoom Meeting: https://tamu.zoom.us/j/93142718312?pwd=c2x5Zm5RZWdheEJ4SU40N3VYMlFpdz09 Meeting ID: 931 4271 8312

Public lectures

Time: 5:00PM - 6:00PM

Location: Zoom

Speaker: Richard Baraniuk, Rice University

Title: Going Off the Deep End with Deep Learning.

Abstract: Video at: https://www.math.tamu.edu/conferences/SIAMTXLA/plenary.html A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale, while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with super-human capabilities. Similar results in language translation, robotics, and games like Chess and Go plus billions of dollars in venture capital have fueled a deep learning bubble and public perception that actual progress is being made towards general artificial intelligence. But fundamental questions remain, such as: Why do deep learning methods work? When do they work? And how can they be fixed when they don't work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures remains elusive. This talk will discuss the important implications of this lack of understanding for consumers, practitioners, and researchers of machine learning. We will also briefly overview recent progress on answering the above questions based on probabilistic graphs and splines.