Tootfinder

Benchmarking Time-localized Explanations for Audio Classification Models
Cecilia Bola\~nos, Leonardo Pepino, Martin Meza, Luciana Ferrer
https://arxiv.org/abs/2506.04391

Benchmarking Time-localized Explanations for Audio Classification Models
Most modern approaches for audio processing are opaque, in the sense that they do not provide an explanation for their decisions. For this reason, various methods have been proposed to explain the outputs generated by these models. Good explanations can result in interesting insights about the data or the model, as well as increase trust in the system. Unfortunately, evaluating the quality of explanations is far from trivial since, for most tasks, there is no clear ground truth explanation to u…

Random-key genetic algorithms
Mariana A. Londe, Luciana S. Pessoa, Carlos E. Andrade, Jos\'e F. Gon\c{c}alves, Mauricio G. C. Resende
https://arxiv.org/abs/2506.02120

Random-key genetic algorithms
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the continuous interval [0, 1). A decoder maps each vector of random keys to a solution of the optimization problem being solved and computes its cost. The benefit of this approach is that all genetic operators and transformations can be maintained within the unitary hypercube, regar…