Search Results for author: Felipe S. Abrahão

Found 4 papers, 0 papers with code

Decoding Geometric Properties in Non-Random Data from First Information-Theoretic Principles

no code implementations13 May 2024 Hector Zenil, Felipe S. Abrahão

Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a univariate signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages from unknown generating sources about which no prior knowledge is available and to which no return message can be sent.

An Optimal, Universal and Agnostic Decoding Method for Message Reconstruction, Bio and Technosignature Detection

no code implementations28 Mar 2023 Hector Zenil, Alyssa Adams, Felipe S. Abrahão

We present a signal reconstruction method for zero-knowledge one-way communication channels in which a receiver aims to interpret a message sent by an unknown source about which no prior knowledge is available and to which no return message can be sent.

A Simplicity Bubble Problem in Formal-Theoretic Learning Systems

no code implementations22 Dec 2021 Felipe S. Abrahão, Hector Zenil, Fabio Porto, Michael Winter, Klaus Wehmuth, Itala M. L. D'Ottaviano

When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity.

BIG-bench Machine Learning

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