[HTML][HTML] Efficient multi-objective neural architecture search framework via policy gradient algorithm

B Lyu, Y Yang, Y Cao, P Wang, J Zhu, J Chang… - Information Sciences, 2024 - Elsevier
Differentiable architecture search plays a prominent role in Neural Architecture Search
(NAS) and exhibits preferable efficiency than traditional heuristic NAS methods, including …

Accelerating deep neural network learning using data stream methodology

P Duda, M Wojtulewicz, L Rutkowski - Information Sciences, 2024 - Elsevier
This paper introduces a novel machine learning approach, called BBATDD (Boosting-Based
Algorithm Trained with Drift Detector), that departs from traditional epoch-based methods by …

Toward Less Constrained Macro-Neural Architecture Search

V Lopes, LA Alexandre - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Networks found with neural architecture search (NAS) achieve the state-of-the-art
performance in a variety of tasks, out-performing human-designed networks. However, most …

Surprisingly Strong Performance Prediction with Neural Graph Features

G Kadlecová, J Lukasik, M Pilát, P Vidnerová… - arXiv preprint arXiv …, 2024 - arxiv.org
Performance prediction has been a key part of the neural architecture search (NAS) process,
allowing to speed up NAS algorithms by avoiding resource-consuming network training …

[HTML][HTML] Guided evolutionary neural architecture search with efficient performance estimation

V Lopes, M Santos, B Degardin, LA Alexandre - Neurocomputing, 2024 - Elsevier
Abstract Neural Architecture Search (NAS) methods have been successfully applied to
image tasks with excellent results. However, NAS methods are often complex and tend to …

[PDF][PDF] Improving Neural Architecture Search With Bayesian Optimization and Generalization Mechanisms

VF Lopes - 2024 - core.ac.uk
Os avanços nos domínios da Inteligência Artificial (IA) e da Aprendizagem Automática (AA)
permitiram obter resultados impressionantes em vários problemas. Estes avanços podem …