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Hector Zenil

Showing results (31-40 of 39) with videos related to

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Frontiers in Computational Neuroscience|June 15, 2026
Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesisEduardo Y Sakabe, Felipe S Abrahão, Alexandre Simões, et al.
Entropy (Basel, Switzerland)|December 3, 2020
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic ComplexityHector Zenil, Santiago Hernández-Orozco, Narsis A Kiani, et al.
Iscience|September 22, 2019
An Algorithmic Information Calculus for Causal Discovery and Reprogramming SystemsHector Zenil, Narsis A Kiani, Francesco Marabita, et al.
Nature Communications|December 13, 2025
Leveraging network motifs to improve artificial neural networksHaoling Zhang, Chao-Han Huck Yang, Hector Zenil, et al.
Bio Systems|March 1, 2018
Corrigendum to "Slime mould: The fundamental mechanisms of biological cognition" [BioSystems 165 (2018) 57-70]Jordi Vallverdú, Oscar Castro, Richard Mayne, et al.
Bio Systems|January 13, 2018
Slime mould: The fundamental mechanisms of biological cognitionJordi Vallverdú, Oscar Castro, Richard Mayne, et al.
Journal of the Royal Society, Interface|June 4, 2025
Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatmentAbbi Abdel-Rehim, Hector Zenil, Oghenejokpeme Orhobor, et al.
Progress in Biophysics and Molecular Biology|August 19, 2017
East-West paths to unconventional computingAndrew Adamatzky, Selim Akl, Mark Burgin, et al.
Frontiers in Artificial Intelligence|February 27, 2026
The future of fundamental science led by generative closed-loop artificial intelligenceHector Zenil, Jesper Tegnér, Felipe S Abrahão, et al.
Pageof 4

Showing results (31-40 of 39) with videos related to

Sort By:
Pageof 4
You have reached the last page of results.This site can display upto 39 results.
Frontiers in Computational Neuroscience|June 15, 2026
Binarized neural networks converge toward algorithmic simplicity: empirical support for the learning-as-compression hypothesisEduardo Y Sakabe, Felipe S Abrahão, Alexandre Simões, et al.
Entropy (Basel, Switzerland)|December 3, 2020
A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic ComplexityHector Zenil, Santiago Hernández-Orozco, Narsis A Kiani, et al.
Iscience|September 22, 2019
An Algorithmic Information Calculus for Causal Discovery and Reprogramming SystemsHector Zenil, Narsis A Kiani, Francesco Marabita, et al.
Nature Communications|December 13, 2025
Leveraging network motifs to improve artificial neural networksHaoling Zhang, Chao-Han Huck Yang, Hector Zenil, et al.
Bio Systems|March 1, 2018
Corrigendum to "Slime mould: The fundamental mechanisms of biological cognition" [BioSystems 165 (2018) 57-70]Jordi Vallverdú, Oscar Castro, Richard Mayne, et al.
Bio Systems|January 13, 2018
Slime mould: The fundamental mechanisms of biological cognitionJordi Vallverdú, Oscar Castro, Richard Mayne, et al.
Journal of the Royal Society, Interface|June 4, 2025
Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatmentAbbi Abdel-Rehim, Hector Zenil, Oghenejokpeme Orhobor, et al.
Progress in Biophysics and Molecular Biology|August 19, 2017
East-West paths to unconventional computingAndrew Adamatzky, Selim Akl, Mark Burgin, et al.
Frontiers in Artificial Intelligence|February 27, 2026
The future of fundamental science led by generative closed-loop artificial intelligenceHector Zenil, Jesper Tegnér, Felipe S Abrahão, et al.
Pageof 4