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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

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Showing 1151–1175 of 9051 papers

TitleStatusHype
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Diverse Image Generation via Self-Conditioned GANsCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
Diverse Video Generation using a Gaussian Process TriggerCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Bias Loss for Mobile Neural NetworksCode1
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
Diversity-Aware Meta Visual PromptingCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Diversity-Guided MLP Reduction for Efficient Large Vision TransformersCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Syn-to-Real GeneralizationCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection ApproachCode1
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive LearningCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
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