4 papers
Toward a mechanistic understanding of inference in visual cortex and diffusion models
Zeyu Yun, Alexander Belsten, Dasheng Bi +3
We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters. The…
When Does Continual Learning Require Learning
Anne Harrington, Nayan Saxena, Michael Murphy +7
As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn? Today, the field largely frames this as a problem o…
Scaling Non-Parametric Sampling with Representation
Vincent Lu, Aaron Truong, Zeyu Yun +1
Scaling and architectural advances have produced strikingly photorealistic image generative models, yet their mechanisms still remain opaque. Rather than advancing scaling, our goa…
URLOST: Unsupervised Representation Learning without Stationarity or Topology
Zeyu Yun, Juexiao Zhang, Yann LeCun +1
Unsupervised representation learning has seen tremendous progress. However, it is constrained by its reliance on domain specific stationarity and topology, a limitation not found i…