This research paper discusses the concept of predictive multiplicity, which refers to the presence of multiple competing models that offer nearly the same level of performance but yield conflicting results…
Continue readingThis paper presents a comparative analysis of noise reduction methods in sentiment analysis on noisy Bengali texts. It introduces a manually annotated dataset (NC-SentNoB) to identify ten different types of…
Continue readingThe study, conducted by a team from the Indian Institute of Technology and National Institute of Technology, focused on using BERT and LightGBM models for binary classification of self-reported COVID-19…
Continue readingThe article presents a study on metalearning and multitask learning, focusing on tasks related by a shared representation in a binary classification setting. The key question is to determine how…
Continue readingThe article presents DE-ViT, a new open-set object detector that uses DINOv2 vision-only backbones. Instead of using language, DE-ViT learns new categories through example images. The authors transform multi-classification tasks…
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