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Mansheej Paul Mansheej Paul About Publications CV Mansheej Paul Email Twitter GitHub Google Scholar Hi! I am a Ph.D. student at Stanford University studying the principles and mechanisms that underlie learning in neural networks. My research focuses on: How does data affect what information and computational abilities are learned by networks? How can we engineer the data distribution to train networks to be more reliable, adaptable, and controllable? Recently, I have worked on using the data distribution and loss landscape properties to make training more efficient, both in terms of requiring less data and training sparser networks. I am currently excited about investigating how the data distribution influences in-context learning, memorization, federated learning and continual learning. I am advised by Surya Ganguli and am an intern at Meta AI . Previously, I was at the RegLab at Stanford University where I worked with partners at the Internal Revenu...
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