2 citations · 3 across the 3 of their papers we have counts for
5 papers
Inference and learning in sparse autoencoders as natural gradient flow
Hadi Vafaii, Tejas Rao, David Chanin +6
Sparse autoencoders are widely used to uncover interpretable features in neural networks, yet reliable recovery remains difficult when features overlap or activate infrequently. Th…
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…
Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps
Christopher J. Kymn, Sonia Mazelet, Anthony Thomas +4
We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information pe…
Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks
Christopher J. Kymn, Sonia Mazelet, Annabel Ng +2
We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequ…
Computing with Residue Numbers in High-Dimensional Representation
Christopher J. Kymn, Denis Kleyko, E. Paxon Frady +4
We introduce Residue Hyperdimensional Computing, a computing framework that unifies residue number systems with an algebra defined over random, high-dimensional vectors. We show ho…