Search Results for author:
Found 7 papers, 2 papers with code
Search Data Structure Learning
In this work, we propose Search Data Structure Learning (SDSL), a generalization of the standard Search Data Structure (SDS) in which the machine has to learn how to search in the database.
A Framework for Studying Reinforcement Learning and Sim-to-Real in Robot Soccer
This article introduces an open framework, called VSSS-RL, for studying Reinforcement Learning (RL) and sim-to-real in robot soccer, focusing on the IEEE Very Small Size Soccer (VSSS) league.
Learning to Play Soccer by Reinforcement and Applying Sim-to-Real to Compete in the Real World
This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS), a traditional league in the Latin American Robotics Competition (LARC).
Adversarial training approach for local data debiasing
The widespread use of automated decision processes in many areas of our society raises serious ethical issues concerning the fairness of the process and the possible resulting discriminations.
Towards Lossless Encoding of Sentences
A lot of work has been done in the field of image compression via machine learning, but not much attention has been given to the compression of natural language.
Fairwashing: the risk of rationalization
Black-box explanation is the problem of explaining how a machine learning model -- whose internal logic is hidden to the auditor and generally complex -- produces its outcomes.
A new approach in machine learning
This enable both the learning algorithm and classifier to be extremely efficient.
