82 citations · 281 across the 31 of their papers we have counts for
5 papers · 1 filter
Neural Manifold Clustering and Embedding
Zengyi Li, Yubei Chen, Yann LeCun +1
Given a union of non-linear manifolds, non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize…
Computing on Functions Using Randomized Vector Representations
E. Paxon Frady, Denis Kleyko, Christopher J. Kymn +2
Vector space models for symbolic processing that encode symbols by random vectors have been proposed in cognitive science and connectionist communities under the names Vector Symbo…
Perceptron Theory Can Predict the Accuracy of Neural Networks
Denis Kleyko, Antonello Rosato, E. Paxon Frady +2
Multilayer neural networks set the current state of the art for many technical classification problems. But, these networks are still, essentially, black boxes in terms of analyzin…
Learning overcomplete, low coherence dictionaries with linear inference
Jesse A. Livezey, Alejandro F. Bujan, Friedrich T. Sommer
Finding overcomplete latent representations of data has applications in data analysis, signal processing, machine learning, theoretical neuroscience and many other fields. In an ov…
Learning in embodied action-perception loops through exploration
Daniel Y. Little, Friedrich T. Sommer
Although exploratory behaviors are ubiquitous in the animal kingdom, their computational underpinnings are still largely unknown. Behavioral Psychology has identified learning as a…