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pro vyhledávání: '"A Antunes"'
Reinforcement learning (RL) has seen significant research and application results but often requires large amounts of training data. This paper proposes two data-efficient off-policy RL methods that use parametrized Q-learning. In these methods, the
Externí odkaz:
http://arxiv.org/abs/2409.11986
The Covid-19 pandemic has affected the world at multiple levels. Data sharing was pivotal for advancing research to understand the underlying causes and implement effective containment strategies. In response, many countries have promoted the availab
Externí odkaz:
http://arxiv.org/abs/2408.17378
Autor:
Antunes, Fabio, Lima, Maria Julia Dias, Araújo, Marco Antônio Pereira, Taibi, Davide, Kalinowski, Marcos
[Context] The adoption of micro-frontends architectures has gained traction as a promising approach to enhance modularity, scalability, and maintainability of web applications. [Goal] The primary aim of this research is to investigate the benefits an
Externí odkaz:
http://arxiv.org/abs/2407.15829
Autor:
Antunes, Diogo S., Oliveira, Afonso N., Breda, André, Franco, Matheus Guilherme, Moniz, Henrique, Rodrigues, Rodrigo
Publikováno v:
In21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24) 2024 (pp. 313-328)
Traditional Byzantine Fault Tolerance (BFT) state machine replication protocols assume a partial synchrony model, leading to a design where a leader replica drives the protocol and is replaced after a timeout. Recently, we witnessed a surge of asynch
Externí odkaz:
http://arxiv.org/abs/2407.14538
Autor:
Novello, Mario, Antunes, Vicente
We present solutions corresponding to rotational configurations in the recently proposed Geometric Scalar Gravity (GSG) theory. The solutions obtained here have the important property that the associated closed time-like curves are always restricted
Externí odkaz:
http://arxiv.org/abs/2407.09663
We develop a semi-analytical model for transport in structured catalytic microreactors, where both reactant and product are compressible fluids. Making use of the lubrication and Fick-Jacobs approximations, we reduce the three-dimensional governing e
Externí odkaz:
http://arxiv.org/abs/2407.03944
Sharing private data for learning tasks is pivotal for transparent and secure machine learning applications. Many privacy-preserving techniques have been proposed for this task aiming to transform the data while ensuring the privacy of individuals. S
Externí odkaz:
http://arxiv.org/abs/2406.16456
Adversarial examples, designed to trick Artificial Neural Networks (ANNs) into producing wrong outputs, highlight vulnerabilities in these models. Exploring these weaknesses is crucial for developing defenses, and so, we propose a method to assess th
Externí odkaz:
http://arxiv.org/abs/2406.14073
Multiple synthetic data generation models have emerged, among which deep learning models have become the vanguard due to their ability to capture the underlying characteristics of the original data. However, the resemblance of the synthetic to the or
Externí odkaz:
http://arxiv.org/abs/2406.02736
We prove that local stable/unstable sets of homeomorphisms of an infinite compact metric space satisfying the gluing-orbit property always contain compact and perfect subsets of the space. As a consequence, we prove that if a positively countably exp
Externí odkaz:
http://arxiv.org/abs/2405.17574