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SUMMARY:CIS Seminar: "Collaborative\, Communal\, & Continual Machine Learning"
DESCRIPTION:Pre-trained models have become a cornerstone of machine learning thanks to the fact that they can provide improved performance with less labeled data on downstream tasks. However\, these models are typically created by resource-rich research groups that unilaterally decide how a given model should be built\, trained\, and released\, after which point it is never updated. In contrast\, open-source development has demonstrated that it is possible for a community of contributors to work together to iteratively build complex and widely used software. This kind of large-scale distributed collaboration is made possible through a mature set of tools including version control and package management. In this talk\, I will discuss a research focus in my group that aims to make it possible to build machine learning models in the way that open-source software is developed. Specifically\, I will discuss our preliminary work on merging multiple models while retaining their individual capabilities\, patching models with cheaply-communicable updates\, designing modular model architectures\, and tracking changes through a version control system for model parameters. I will conclude with an outlook on how the field will change once truly collaborative\, communal\, and continual machine learning is possible.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-collaborative-communal-continual-machine-learning/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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