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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 231240 of 9051 papers

TitleStatusHype
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Conditional GANs with Auxiliary Discriminative ClassifierCode2
ProtComposer: Compositional Protein Structure Generation with 3D EllipsoidsCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based PoliciesCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
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