303 citations · 673 across the 10 of their papers we have counts for
5 papers · 1 filter
Sparse Prototype Code Underlies Classification and Prediction Across Modalities
Yehonatan Avidan, Daniel D. Lee, Haim Sompolinsky
Neural representations have become a central tool for studying the internal mechanisms of modern AI models, yet their complex high-dimensional structure makes them difficult to int…
Parametric Matrix Models
Patrick Cook, Danny Jammooa, Morten Hjorth-Jensen +2
We present a general class of machine learning algorithms called parametric matrix models. In contrast with most existing machine learning models that imitate the biology of neuron…
TimewarpVAE: Simultaneous Time-Warping and Representation Learning of Trajectories
Travers Rhodes, Daniel D. Lee
Human demonstrations of trajectories are an important source of training data for many machine learning problems. However, the difficulty of collecting human demonstration data for…
Sparsity-depth Tradeoff in Infinitely Wide Deep Neural Networks
Chanwoo Chun, Daniel D. Lee
We investigate how sparse neural activity affects the generalization performance of a deep Bayesian neural network at the large width limit. To this end, we derive a neural network…
Learning Data Manifolds with a Cutting Plane Method
SueYeon Chung, Uri Cohen, Haim Sompolinsky +1
We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmenta…