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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 14611470 of 9051 papers

TitleStatusHype
Neural Ensemble Search for Uncertainty Estimation and Dataset ShiftCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Adversarial Parametric Pose PriorCode1
Neural Multi-Objective Combinatorial Optimization with Diversity EnhancementCode1
NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of GarmentsCode1
Neural Video Compression with Diverse ContextsCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
Chain-of-Choice Hierarchical Policy Learning for Conversational RecommendationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
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