107 citations · 185 across the 13 of their papers we have counts for
13 papers
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
Daniel Severo, James Townsend, Ashish Khisti +1
We present a one-shot method for compressing large labeled graphs called Random Edge Coding. When paired with a parameter-free model based on Pólya's Urn, the worst-case computatio…
Verified Reversible Programming for Verified Lossless Compression
James Townsend, Jan-Willem van de Meent
Lossless compression implementations typically contain two programs, an encoder and a decoder, which are required to be inverse to one another. We observe that a significant class…
Parallel Neural Local Lossless Compression
Mingtian Zhang, James Townsend, Ning Kang +1
The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) gen…
Adaptive Optimization with Examplewise Gradients
Julius Kunze, James Townsend, David Barber
We propose a new, more general approach to the design of stochastic gradient-based optimization methods for machine learning. In this new framework, optimizers assume access to a b…
Compressing Multisets with Large Alphabets using Bits-Back Coding
Daniel Severo, James Townsend, Ashish Khisti +2
Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real…
Lossless Compression with Latent Variable Models
James Townsend
We develop a simple and elegant method for lossless compression using latent variable models, which we call 'bits back with asymmetric numeral systems' (BB-ANS). The method involve…