Topics in Deep Learning Theory
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesTopics in Deep Learning Theory Eli Grigsby
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This is a course on geometric aspects of deep learning theory. Broadly speaking, we’ll investigate the question: How might human-interpretable concepts be expressed in the geometry of their data encodings, and how does this geometry interact with the computational units and higher-level algebraic structures in various parameterized function classes, especially neural network classes?
Sep. 10-Nov. 1: Tuesdays and Thursdays 2:30-3:45 pm, Harvard CMSA Math & ML program, 20 Garden St; Cambridge, Room G10
During the portion of the course Sep. 10-Nov. 1 presented as part of the Math and Machine Learning program, we will explore the current state of research on mechanistic interpretability of transformers, the architecture underlying large language models like Chat-GPT.
Topics in Deep Learning Theory
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesTopics in Deep Learning Theory Eli Grigsby
Topics in Deep Learning Theory
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesTopics in Deep Learning Theory Eli Grigsby
Topics in Deep Learning Theory
CMSA Room G10 CMSA, 20 Garden Street, Cambridge, MA, United StatesTopics in Deep Learning Theory Eli Grigsby