Bridging the Gap Between Deep Learning Theory and Practice

#DeepLearning #AlgorithmLearning #ArtificialIntelligence #NeuralNetworks #Generalization
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IEEE CS SD Invited Seminar Series 2024 - Lecture 4

Despite the widespread proliferation of neural networks, the mechanisms through which they operate so successfully are not well understood.  In this talk, we will first explore empirical and theoretical investigations into neural network training and generalization and what they can tell us about why deep learning works.  Then, we will examine a recent line of work on algorithm learning.  While neural networks typically excel at pattern matching tasks, we consider whether neural networks can learn algorithms that scale to problem instances orders of magnitude larger than those seen during training.


  Date and Time

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  • Date: 23 Apr 2024
  • Time: 05:30 PM to 06:30 PM
  • All times are (UTC-07:00) Pacific Time (US & Canada)
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  • Media Partner: Open Research Institute

  • Starts 06 March 2024 12:00 AM
  • Ends 23 April 2024 06:30 PM
  • All times are (UTC-07:00) Pacific Time (US & Canada)
  • No Admission Charge

  Speakers

Micah Goldblum of New York University

Topic:

Bridging the Gap Between Deep Learning Theory and Practice

Biography:

 

Micah Goldblum is a postdoctoral researcher at New York University with Yann LeCun and Andrew Gordon Wilson. His research portfolio includes award winning work in Bayesian inference, generalization theory, algorithmic reasoning, and AI security and privacy. Micah’s paper on model comparison received the Outstanding Paper Award at ICML 2022. Before his current position, he received a Ph.D. in mathematics at the University of Maryland where he worked with Tom Goldstein and Wojciech Czaja.

 

 

Address:New York, United States




Agenda

- Invited talk from Micah Goldblum, postdoctoral research fellow at New York University working with Yann LeCun and Andrew Gordon Wilson.

- Q/A Session


- 4th lecture of the 2024 Invited Seminar Series (Virtual) organized by IEEE Computer Society San Diego Chapter. Previous lectures: 2023 and 2024 invited seminar series.