286 citations · 823 across the 37 of their papers we have counts for
8 papers · 1 filter
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…
Neuromorphic Visual Scene Understanding with Resonator Networks
Alpha Renner, Lazar Supic, Andreea Danielescu +5
Analyzing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one maj…
Disentangling images with Lie group transformations and sparse coding
Ho Yin Chau, Frank Qiu, Yubei Chen +1
Discrete spatial patterns and their continuous transformations are two important regularities contained in natural signals. Lie groups and representation theory are mathematical to…
Resonator networks for factoring distributed representations of data structures
E. Paxon Frady, Spencer Kent, Bruno A. Olshausen +1
The ability to encode and manipulate data structures with distributed neural representations could qualitatively enhance the capabilities of traditional neural networks by supporti…
Dynamic Scale Inference by Entropy Minimization
Dequan Wang, Evan Shelhamer, Bruno Olshausen +1
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate…
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies
Alexander G. Anderson, Cory P. Berg, Daniel P. Mossing +1
A recent paper by Gatys et al. describes a method for rendering an image in the style of another image. First, they use convolutional neural network features to build a statistical…
Learning sparse representations of depth
Ivana Tosic, Bruno A. Olshausen, Benjamin J. Culpepper
This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationar…
An Unsupervised Algorithm For Learning Lie Group Transformations
Jascha Sohl-Dickstein, Ching Ming Wang, Bruno A. Olshausen
We present several theoretical contributions which allow Lie groups to be fit to high dimensional datasets. Transformation operators are represented in their eigen-basis, reducing…