5 papers
Tighter Bounds for Algorithmic Complexity Estimation Using a Reusable Code-Based Block Decomposition Method
Eduardo Yuji Sakabe, Felipe S. Abrahão, Santiago Hernández-Orozco +2
The Block Decomposition Method (BDM) was introduced as an alternative to popular lossless compression methods such as LZW for estimating algorithmic complexity from the principles…
World Machine: Towards Generative World Modeling for Time-Series
Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini +2
World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured and generalizable way. We pr…
Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis
Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões +4
Understanding and controlling the informational complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and mo…
InstructRobot: A Model-Free Framework for Mapping Natural Language Instructions into Robot Motion
Iury Cleveston, Alana C. Santana, Paula D. P. Costa +3
The ability to communicate with robots using natural language is a significant step forward in human-robot interaction. However, accurately translating verbal commands into physica…
Building a Cognitive Twin Using a Distributed Cognitive System and an Evolution Strategy
Wandemberg Gibaut, Ricardo Gudwin
This work presents a technique to build interaction-based Cognitive Twins (a computational version of an external agent) using input-output training and an Evolution Strategy on to…