18 citations · 45 across the 21 of their papers we have counts for
14 papers · 1 filter
Singular Relative Entropy Coding with Bits-Back Rejection Sampling
Gergely Flamich, Spencer Hill
A relative entropy code for a source is a stochastic code that encodes random samples from a prescribed using as few bits as possible. A generalisation…
Rejection Sampling is Optimal for Relative Entropy Coding
Spencer Hill, Fady Alajaji, Tamás Linder +1
In relative entropy coding, a sender aims to design a stochastic code such that, on input , the receiver can generate a sample . It is a standard r…
Data Compression with Stochastic Codes
Gergely Flamich, Deniz Gündüz
Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry. This paper describes one such dir…
Data Compression with Relative Entropy Coding
Gergely Flamich
Over the last few years, machine learning unlocked previously infeasible features for compression, such as providing guarantees for users' privacy or tailoring compression to speci…
The Redundancy of Non-Singular Channel Simulation
Gergely Flamich, Sharang M. Sriramu, Aaron B. Wagner
Channel simulation is an alternative to quantization and entropy coding for performing lossy source coding. Recently, channel simulation has gained significant traction in both the…
Getting Free Bits Back from Rotational Symmetries in LLMs
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Current methods for compressing neural network weights, such as decomposition, pruning, quantization, and channel simulation, often overlook the inherent symmetries within these ne…
Some Notes on the Sample Complexity of Approximate Channel Simulation
Gergely Flamich, Lennie Wells
Channel simulation algorithms can efficiently encode random samples from a prescribed target distribution and find applications in machine learning-based lossy data compression…
Accelerating Relative Entropy Coding with Space Partitioning
Jiajun He, Gergely Flamich, José Miguel Hernández-Lobato
Relative entropy coding (REC) algorithms encode a random sample following a target distribution , using a coding distribution shared between the sender and receiver. Sadly,…
On Channel Simulation with Causal Rejection Samplers
Daniel Goc, Gergely Flamich
One-shot channel simulation has recently emerged as a promising alternative to quantization and entropy coding in machine-learning-based lossy data compression schemes. However, wh…
Faster Relative Entropy Coding with Greedy Rejection Coding
Gergely Flamich, Stratis Markou, Jose Miguel Hernandez Lobato
Relative entropy coding (REC) algorithms encode a sample from a target distribution using a proposal distribution using as few bits as possible. Unlike entropy coding, REC…
Greedy Poisson Rejection Sampling
Gergely Flamich
One-shot channel simulation is a fundamental data compression problem concerned with encoding a single sample from a target distribution using a coding distribution using a…
Adaptive Greedy Rejection Sampling
Gergely Flamich, Lucas Theis
We consider channel simulation protocols between two communicating parties, Alice and Bob. First, Alice receives a target distribution , unknown to Bob. Then, she employs a shar…
Fast Relative Entropy Coding with A* coding
Gergely Flamich, Stratis Markou, José Miguel Hernández-Lobato
Relative entropy coding (REC) algorithms encode a sample from a target distribution using a proposal distribution , such that the expected codelength is $\mathcal{O}(D_{KL}[…
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression meth…