UMN Machine Learning Seminar
The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Thursday from 12 p.m. - 1 p.m. during the Summer 2021 semester.
This week's speaker, Chiyuan Zhang (Google Brain) will be giving a talk titled "Characterizing Structural Regularities of Labeled Data in Overparameterized Models."
Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station does not accept payment in advance. Likewise, deep neural networks can generalize across instances that share common patterns or structures, yet have the capacity to memorize rare or irregular forms. We analyze how individual instances are treated by a model via a consistency score. The score characterizes the expected accuracy for a held-out instance given training sets of varying size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple data sets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and strongly regular examples at the other end. We identify computationally inexpensive proxies to the consistency score using statistics collected during training. We show examples of potential applications to the analysis of deep-learning systems.
Chiyuan Zhang is a research scientist at Google Research, Brain Team. He is interested in analyzing and understanding the foundations behind the effectiveness of deep learning, as well as its connection to the cognition and learning mechanisms of the human brain. Chiyuan Zhang holds a Ph.D. from MIT (2017, advised by Tomaso Poggio), and a Bachelor (2009) and a Master (2012) degrees in computer science from Zhejiang University, China. His work was recognized by INTERSPEECH best student paper award in 2014, and ICLR best paper award in 2017.