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05/02/2023
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold
We develop information-geometric techniques to analyze the trajectories ...
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10/31/2022
A picture of the space of typical learnable tasks
We develop a technique to analyze representations learned by deep networ...
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08/23/2022
The Value of Out-of-Distribution Data
More data helps us generalize to a task. But real datasets can contain o...
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02/01/2022
Deep Reference Priors: What is the best way to pretrain a model?
What is the best way to exploit extra data – be it unlabeled data from t...
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06/06/2021
Boosting a Model Zoo for Multi-Task and Continual Learning
Leveraging data from multiple tasks, either all at once, or incrementall...
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09/09/2019
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Option discovery and skill acquisition frameworks are integral to the fu...
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05/14/2019
Successor Options: An Option Discovery Framework for Reinforcement Learning
The options framework in reinforcement learning models the notion of a s...
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06/12/2018
A Question-Answering framework for plots using Deep learning
Deep Learning has managed to push boundaries in a wide variety of tasks....
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11/27/2017
AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings
We consider the problem of computing a matching in a bipartite graph in ...
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05/20/2017
