Search Results for author:
Found 8 papers, 3 papers with code
The Value of Out-of-Distribution Data
We demonstrate a counter-intuitive phenomenon for such problems: generalization error of the task can be a non-monotonic function of the number of OOD samples; a small number of OOD samples can improve generalization but if the number of OOD samples is beyond a threshold, then the generalization error can deteriorate.
Deep Reference Priors: What is the best way to pretrain a model?
Such priors enable the task to maximally affect the Bayesian posterior, e. g., reference priors depend upon the number of samples available for learning the task and for very small sample sizes, the prior puts more probability mass on low-complexity models in the hypothesis space.
Model Zoo: A Growing Brain That Learns Continually
This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models.
Model Zoo: A Growing "Brain" That Learns Continually
We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them.
Ranked #1 on
Continual Learning
on Cifar100 (20 tasks)
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent.
Successor Options: An Option Discovery Framework for Reinforcement Learning
This work adopts a complementary approach, where we attempt to discover options that navigate to landmark states.
FigureNet: A Deep Learning model for Question-Answering on Scientific Plots
Deep Learning has managed to push boundaries in a wide variety of tasks.
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning
Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains.
