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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 761770 of 9051 papers

TitleStatusHype
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Syn-to-Real GeneralizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
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