Search Results for author:
Found 13 papers, 12 papers with code
Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs
We present a one-shot method for compressing large labeled graphs called Random Edge Coding.
Verified Reversible Programming for Verified Lossless Compression
Agda supports formal verification of program properties, and the compiler for our reversible language (which is implemented as an Agda macro), produces not just an encoder/decoder pair of functions but also a proof that they are inverse to one another.
Parallel Neural Local Lossless Compression
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) generalization performance in the image compression task.
Adaptive Optimization with Examplewise Gradients
We propose a new, more general approach to the design of stochastic gradient-based optimization methods for machine learning.
Compressing Multisets with Large Alphabets using Bits-Back Coding
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-world settings.
Lossless Compression with Latent Variable Models
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).
Lossless compression with state space models using bits back coding
We generalize the 'bits back with ANS' method to time-series models with a latent Markov structure.
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Naively applied, our schemes would require more initial bits than the standard bits-back coder, but we show how to drastically reduce this additional cost with couplings in the latent space.
A tutorial on the range variant of asymmetric numeral systems
This paper is intended to be an accessible introduction to the range variant of Asymmetric Numeral Systems (rANS).
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
We make the following striking observation: fully convolutional VAE models trained on 32x32 ImageNet can generalize well, not just to 64x64 but also to far larger photographs, with no changes to the model.
Practical Lossless Compression with Latent Variables using Bits Back Coding
Deep latent variable models have seen recent success in many data domains.
Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation
Optimization on manifolds is a class of methods for optimization of an objective function, subject to constraints which are smooth, in the sense that the set of points which satisfy the constraints admits the structure of a differentiable manifold.
The VC-Dimension of Similarity Hypotheses Spaces
Given a set $X$ and a function $h:X\longrightarrow\{0, 1\}$ which labels each element of $X$ with either $0$ or $1$, we may define a function $h^{(s)}$ to measure the similarity of pairs of points in $X$ according to $h$.
