One-class lightweight interpretable filtering for academic profiles and strategic themes affinity (2025)
- Authors:
- USP affiliated authors: GÔLO, MARCOS PAULO SILVA - ICMC ; UTINO, MATHEUS YASUO RIBEIRO - ICMC
- Unidade: ICMC
- DOI: 10.1109/SBAC-PADW69789.2025.00028
- Subjects: PROCESSAMENTO DE LINGUAGEM NATURAL; APRENDIZADO COMPUTACIONAL; SUSTENTABILIDADE; MINERAÇÃO DE DADOS
- Keywords: One-Class Classification; Lightweight Filtering; Language Models; Bidirectional Encoder Representations for Transformers; Text Pair Classification; Lightweight Methods
- Agências de fomento:
- Language: Inglês
- Objetivos de Desenvolvimento Sustentável (ODS):
09. Indústria, inovação e infraestrutura
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2025
- Source:
- Título: Proceedings
- Conference titles: IEEE/SBC International Symposium on Computer Architecture and High Performance Computing Workshops - SBAC-PADW
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
GÔLO, Marcos Paulo Silva e UTINO, Matheus Yasuo Ribeiro. One-class lightweight interpretable filtering for academic profiles and strategic themes affinity. 2025, Anais.. Los Alamitos: IEEE, 2025. Disponível em: https://doi.org/10.1109/SBAC-PADW69789.2025.00028. Acesso em: 20 mar. 2026. -
APA
Gôlo, M. P. S., & Utino, M. Y. R. (2025). One-class lightweight interpretable filtering for academic profiles and strategic themes affinity. In Proceedings. Los Alamitos: IEEE. doi:10.1109/SBAC-PADW69789.2025.00028 -
NLM
Gôlo MPS, Utino MYR. One-class lightweight interpretable filtering for academic profiles and strategic themes affinity [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/SBAC-PADW69789.2025.00028 -
Vancouver
Gôlo MPS, Utino MYR. One-class lightweight interpretable filtering for academic profiles and strategic themes affinity [Internet]. Proceedings. 2025 ;[citado 2026 mar. 20 ] Available from: https://doi.org/10.1109/SBAC-PADW69789.2025.00028 - Efficient filtering with BERT embeddings for researcher-topic affinity prediction in HPC pipelines
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