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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 821830 of 9051 papers

TitleStatusHype
Graph Neural PDE Solvers with Conservation and Similarity-EquivarianceCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Contrastive Syn-to-Real GeneralizationCode1
ARGS: Alignment as Reward-Guided SearchCode1
Argumentative Large Language Models for Explainable and Contestable Claim VerificationCode1
Controllable Multi-Interest Framework for RecommendationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
GUARD: A Safe Reinforcement Learning BenchmarkCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
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