Julian Burella Pérez

dblp:262/3897 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—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.

Artificial intelligence
1 paper
Graph learning · 77% Deep learning architectures and training · 23%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Graph learning
topological data analysis
0.512021
giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration · J. Mach. Learn. Res. 2021

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

scikit-learn API · 0.5persistent homology · 0.5
YearPublicationVenuePosition
2021 giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration
abstract
We introduce giotto-tda, a Python library that integrates high-performance topological data analysis with machine learning via a scikit-learn-compatible API and state-of-the-art C++ implementations. The library's ability to handle various types of data is rooted in a wide range of preprocessing techniques, and its strong focus on data exploration and interpretability is aided by an intuitive plotting API. Source code, binaries, examples, and documentation can be found at https://github.com/giotto-ai/giotto-tda.
Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess
J. Mach. Learn. Res.4