5 citations · 10 across the 6 of their papers we have counts for
6 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…
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Yubei Chen, Zeyu Yun, Yi Ma +2
We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that ach…
Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors
Zeyu Yun, Yubei Chen, Bruno A Olshausen +1
Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers,…