303 citations · 673 across the 10 of their papers we have counts for
10 papers
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…
A theory of learning with constrained weight-distribution
Weishun Zhong, Ben Sorscher, Daniel D Lee +1
A central question in computational neuroscience is how structure determines function in neural networks. The emerging high-quality large-scale connectomic datasets raise the quest…
Classification and Geometry of General Perceptual Manifolds
SueYeon Chung, Daniel D. Lee, Haim Sompolinsky
Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location…