3 papers
cs.LG2026
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
Waqas Ahmed, Sheeba Samuel, Kevin Coakley +2
To enhance the reproducibility and reliability of deep learning models, we address a critical gap in current training methodologies: the lack of mechanisms that ensure consistent a…
cs.CV2024
Explainability of Deep Learning-Based Plant Disease Classifiers Through Automated Concept Identification
Jihen Amara, Birgitta König-Ries, Sheeba Samuel
While deep learning has significantly advanced automatic plant disease detection through image-based classification, improving model explainability remains crucial for reliable dis…
cs.IR2024
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publications
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
Deep Learning (DL) techniques are increasingly applied in scientific studies across various domains to address complex research questions. However, the methodological details of th…