Andrew K Lampinen — accepted AI-conference papers

2024

SODA: Bottleneck Diffusion Models for Representation Learning

Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland +3

CVPR 2024poster

We introduce SODA a self-supervised diffusion model designed for representation learning. The model incorporates an image encoder which distills a source view into a compact representation that in turn guides the generation of related novel views. We show that by imposing a tight bottleneck between…

Cited by 43 Source ↗PDF ↗code ↗Scholar ↗

2022

Tell me why! Explanations support learning relational and causal structure

Andrew K Lampinen, Nicholas Roy, Ishita Dasgupta, Stephanie Cy Chan, Allison Tam, James Mcclelland +5

ICML 2022spotlight

Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language{—}particularly in the form of explanations{—}plays a considerable role in overcoming this challenge. Here, we show that language can play a similar ro…

Cited by 46 Source ↗PDF ↗code ↗Scholar ↗

2019

An analytic theory of generalization dynamics and transfer learning in deep linear networks

Andrew K. Lampinen, Surya Ganguli

ICLR 2019poster

Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge…

Cited by 136 Source ↗PDF ↗Scholar ↗