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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 3521–3530 of 9051 papers

TitleStatusHype
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses—0
DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer—0
Diversified Mutual Learning for Deep Metric Learning—0
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests—0
Extracting Radiological Findings With Normalized Anatomical Information Using a Span-Based BERT Relation Extraction Model—0
Extracting Signals of Higgs Boson From Background Noise Using Deep Neural Networks—0
Extraction and Evaluation of Formulaic Expressions Used in Scholarly Papers—0
An Inclusive Foundation Model for Generalizable Cytogenetics in Precision Oncology—0
Diversified Multiscale Graph Learning with Graph Self-Correction—0
Diversified Late Acceptance Search—0
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