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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 14511460 of 9051 papers

TitleStatusHype
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Multimodal Shape Completion via Conditional Generative Adversarial NetworksCode1
Multi-Objective Evolutionary Design of Composite Data-Driven ModelsCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
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