Hector Zenil
researcher
Facts
9 properties- Instance of
- human
- Date of birth
- 1977
- Occupation
- researcher, mathematician, computer scientist
- Doctoral advisor
- Jean-Paul Delahaye, Cristian Calude[5][6]
- Doctoral student
- Santiago Hernández-Orozco[8]
- Family name
- Zenil[9]
- Maintained by WikiProject
- WikiProject Mathematics[11]
🏷️ Also known as
en Hector Zenil
en Héctor Zenil
🔖 Identifiers · 17
- ISNI
- 0000000123305016
- ORCID iD
- 0000-0003-0634-4384
- VIAF cluster ID
- 173031691
- Library of Congress authority ID
- no2011107074
- GND ID
- 1011480018
- DBLP author ID
- z/HectorZenilChavez
- DDB person (GND) ID
- 1011480018
- IdRef ID
- 15205149X
- MR Author ID
- 851638
- Mathematics Genealogy Project ID
- 93955
- NL CR AUT ID
- mub2011670479
- NLab ID
- Hector Zenil
Show all 17 identifiers +5
- National Library of Israel J9U ID
- 987007595681005171
- Parsifal cluster ID
- 513071
- Scopus author ID
- 38863598800
- Yale LUX ID
- person/9b6b2ae3-7fee-466f-bce2-4445b0435e79
- ZbMATH author ID
- zenil.hector
🔗 Connections
Affiliated with 14
Author of 11
A Computable Measure of Algorithmic Probability by Finite Approximations with an Application to Integer Sequences,
Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity,
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces,
Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution,
Causal deconvolution by algorithmic generative models,
Complexity Measurement Based on Information Theory and Kolmogorov Complexity,
Emergence and algorithmic information dynamics of systems and observers,
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
+3 more
← Authored by 11
A Computable Measure of Algorithmic Probability by Finite Approximations with an Application to Integer Sequences,
Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity,
Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces,
Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution,
Causal deconvolution by algorithmic generative models,
Complexity Measurement Based on Information Theory and Kolmogorov Complexity,
Emergence and algorithmic information dynamics of systems and observers,
Formal Definitions of Unbounded Evolution and Innovation Reveal Universal Mechanisms for Open-Ended Evolution in Dynamical Systems
+3 more
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