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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 776–800 of 9051 papers

TitleStatusHype
Fine-Grained VR Sketching: Dataset and InsightsCode1
ARGS: Alignment as Reward-Guided SearchCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Controllable Group Choreography using Contrastive DiffusionCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
ARBERT & MARBERT: Deep Bidirectional Transformers for ArabicCode1
Forecasting Future World Events with Neural NetworksCode1
Fork or Fail: Cycle-Consistent Training with Many-to-One MappingsCode1
Argumentative Large Language Models for Explainable and Contestable Claim VerificationCode1
Controllable Multi-Interest Framework for RecommendationCode1
Controllable Video Captioning with an Exemplar SentenceCode1
FreEformer: Frequency Enhanced Transformer for Multivariate Time Series ForecastingCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
FS6D: Few-Shot 6D Pose Estimation of Novel ObjectsCode1
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
Contrastive Syn-to-Real GeneralizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
GenAug: Data Augmentation for Finetuning Text GeneratorsCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
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