17 citations · 30 across the 5 of their papers we have counts for
6 papers
Almost Uniform Sampling From Neural Networks
Changlong Wu, Narayana Prasad Santhanam
Given a length sample from and a neural network with a fixed architecture with weights, neurons, linear threshold activation functions, and binary output…
Thermodynamic Computing
Tom Conte, Erik DeBenedictis, Natesh Ganesh +36
The hardware and software foundations laid in the first half of the 20th Century enabled the computing technologies that have transformed the world, but these foundations are now u…
Redundancy of unbounded memory Markov classes with continuity conditions
Changlong Wu, Maryam Hosseini, Narayana Santhanam
We study the redundancy of universally compressing strings generated by a binary Markov source without any bound on the memory. To better understand the connec…
Optimal Lempel-Ziv based lossy compression for memoryless data: how to make the right mistakes
Narayana Santhanam, Dharmendra Modha
Compression refers to encoding data using bits, so that the representation uses as few bits as possible. Compression could be lossless: i.e. encoded data can be recovered exactly f…
On Modeling Profiles instead of Values
Alon Orlitsky, Narayana Santhanam, Krishnamurthy Viswanathan +1
We consider the problem of estimating the distribution underlying an observed sample of data. Instead of maximum likelihood, which maximizes the probability of the ob served values…
Information-theoretic limits of selecting binary graphical models in high dimensions
Narayana Santhanam, Martin J. Wainwright
The problem of graphical model selection is to correctly estimate the graph structure of a Markov random field given samples from the underlying distribution. We analyze the inform…