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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

Showing 42114220 of 9051 papers

TitleStatusHype
KPEval: Towards Fine-Grained Semantic-Based Keyphrase EvaluationCode1
Retrievability in an Integrated Retrieval System: An Extended Study0
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
Exploring Novel Quality Diversity Methods For Generalization in Reinforcement Learning0
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste SortingCode1
Guiding AI-Generated Digital Content with Wireless Perception0
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Optimizing the Procedure of CT Segmentation Labeling0
Application-Driven AI Paradigm for Person Counting in Various Scenarios0
Towards Diverse and Coherent Augmentation for Time-Series ForecastingCode1
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