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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 3231–3240 of 9051 papers

TitleStatusHype
CTR-Guided Generative Query Suggestion in Conversational Search—0
CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis—0
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models—0
CTAP for Italian: Integrating Components for the Analysis of Italian into a Multilingual Linguistic Complexity Analysis Tool—0
Augmenting Zero-Shot Detection Training with Image Labels—0
CSSDH: An Ontology for Social Determinants of Health to Operational Continuity of Care Data Interoperability—0
cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model—0
Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition—0
CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models—0
CSECU-DSG at SemEval-2022 Task 11: Identifying the Multilingual Complex Named Entity in Text Using Stacked Embeddings and Transformer based Approach—0
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