107 citations · 221 across the 17 of their papers we have counts for
13 papers · 1 filter
User-defined Event Sampling and Uncertainty Quantification in Diffusion Models for Physical Dynamical Systems
Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson +2
Diffusion models are a class of probabilistic generative models that have been widely used as a prior for image processing tasks like text conditional generation and inpainting. We…
Possibility Before Utility: Learning And Using Hierarchical Affordances
Robby Costales, Shariq Iqbal, Fei Sha
Reinforcement learning algorithms struggle on tasks with complex hierarchical dependency structures. Humans and other intelligent agents do not waste time assessing the utility of…
Policy Learning and Evaluation with Randomized Quasi-Monte Carlo
Sebastien M. R. Arnold, Pierre L'Ecuyer, Liyu Chen +2
Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration. These integrals are rarely analytica…
HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks
Filipe de Avila Belbute-Peres, Yi-fan Chen, Fei Sha
Many types of physics-informed neural network models have been proposed in recent years as approaches for learning solutions to differential equations. When a particular task requi…
Embedding Adaptation is Still Needed for Few-Shot Learning
Sébastien M. R. Arnold, Fei Sha
Constructing new and more challenging tasksets is a fruitful methodology to analyse and understand few-shot classification methods. Unfortunately, existing approaches to building t…
Drinking from a Firehose: Continual Learning with Web-scale Natural Language
Hexiang Hu, Ozan Sener, Fei Sha +1
Continual learning systems will interact with humans, with each other, and with the physical world through time -- and continue to learn and adapt as they do. An important open pro…