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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 641650 of 9051 papers

TitleStatusHype
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Bias Loss for Mobile Neural NetworksCode1
An Extensible Benchmark Suite for Learning to Simulate Physical SystemsCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Biological Sequence Design with GFlowNetsCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
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