Evolving Long Short-Term Memory Networks

Author(s)

Neto, Vicente Coelho Lobo ; Passos, Leandro Aparecido ; Papa, Joao Paulo ; Krzhizhanovskaya, VV ; Zavodszky, G ; Lees, MH ; Dongarra, JJ ; Sloot, PMA ; Brissos, S ; Teixeira, J

• Document type: Journal article
• Source: COMPUTATIONAL SCIENCE - ICCS 2020, PT II; v. 12138, p. 14-pg., 2020-01-01.

Abstract

Machine learning techniques have been massively employed in the last years over a wide variety of applications, especially those based on deep learning, which obtained state-of-the-art results in several research fields. Despite the success, such techniques still suffer from some shortcomings, such as the sensitivity to their hyperparameters, whose proper selection is context-dependent, i.e., the model may perform better over each dataset when using a specific set of hyperparameters. Therefore, we propose an approach based on evolutionary optimization techniques for fine-tuning Long Short-Term Memory networks. Experiments were conducted over three public word-processing datasets for part-of-speech tagging. The results showed the robustness of the proposed approach for the aforementioned task. (AU)

• FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
• Grantee: João Paulo Papa
• Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
• FAPESP's process: 18/10100-6 - Hyperparameter fine-tuning in long short-term memory nets using genetic programming
• Grantee: Vicente Coelho Lobo Neto
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program