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Giulio Zani

dblp:382/3607 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › source coding
entropy coding
0.812024
Entropy Coding of Unordered Data Structures · ICLR 2024
Coding theory
source coding
0.812024
Entropy Coding of Unordered Data Structures · ICLR 2024

Methods — techniques the papers use, named apart from their topics

bits-back coding · 0.8
YearPublicationVenuePosition
2024 Entropy Coding of Unordered Data Structures
abstract
We present shuffle coding, a general method for optimal compression of sequences of unordered objects using bits-back coding. Data structures that can be compressed using shuffle coding include multisets, graphs, hypergraphs, and others. We release an implementation that can easily be adapted to different data types and statistical models, and demonstrate that our implementation achieves state-of-the-art compression rates on a range of graph datasets including molecular data.
Julius Kunze, Daniel Severo 0001, Giulio Zani, Jan-Willem van de Meent, James Townsend
ICLR3