Search Results for author: Marcelo Matheus Gauy

Found 7 papers, 4 papers with code

Acoustic models of Brazilian Portuguese Speech based on Neural Transformers

no code implementations14 Dec 2023 Marcelo Matheus Gauy, Marcelo Finger

In this work, we build an acoustic model of Brazilian Portuguese Speech through a Transformer neural network.

Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19

1 code implementation25 Oct 2022 Marcelo Matheus Gauy, Marcelo Finger

This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples.

The Influence of Memory in Multi-Agent Consensus

1 code implementation10 May 2021 David Kohan Marzagão, Luciana Basualdo Bonatto, Tiago Madeira, Marcelo Matheus Gauy, Peter McBurney

Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consensus state.

Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions

no code implementations11 Oct 2019 Florian Meier, Asier Mujika, Marcelo Matheus Gauy, Angelika Steger

Finally, we evaluate our approach empirically on MNIST and reinforcement learning tasks and show that it considerably improves the gradient estimation of ES at no extra computational cost.

reinforcement-learning Reinforcement Learning (RL)

Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning

1 code implementation11 Feb 2019 Frederik Benzing, Marcelo Matheus Gauy, Asier Mujika, Anders Martinsson, Angelika Steger

In contrast, the online training algorithm Real Time Recurrent Learning (RTRL) provides untruncated gradients, with the disadvantage of impractically large computational costs.

Memorization

The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates

no code implementations16 Aug 2018 Hafsteinn Einarsson, Marcelo Matheus Gauy, Johannes Lengler, Florian Meier, Asier Mujika, Angelika Steger, Felix Weissenberger

For the first setup, we give a schedule that achieves a runtime of $(1\pm o(1))\beta n \ln n$, where $\beta \approx 3. 552$, which is an asymptotic improvement over the runtime of the static setup.

Evolutionary Algorithms

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