A Probabilistic Framework for Modular Continual Learning

no code implementations • 11 Jun 2023 • Lazar Valkov, Akash Srivastava, Swarat Chaudhuri, Charles Sutton

To address this challenge, we develop a modular CL framework, called PICLE, that accelerates search by using a probabilistic model to cheaply compute the fitness of each composition.

Continual Learning

HOUDINI: Lifelong Learning as Program Synthesis

2 code implementations • NeurIPS 2018 • Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, Swarat Chaudhuri

We present a neurosymbolic framework for the lifelong learning of algorithmic tasks that mix perception and procedural reasoning.

Program Synthesis Transfer Learning

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

1 code implementation • NeurIPS 2017 • Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, Charles Sutton

Deep generative models provide powerful tools for distributions over complicated manifolds, such as those of natural images.