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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 11311140 of 9051 papers

TitleStatusHype
Controllable Group Choreography using Contrastive DiffusionCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Differential Evolution with Reversible Linear TransformationsCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
Contrastive Syn-to-Real GeneralizationCode1
Controllable Multi-Interest Framework for RecommendationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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