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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 14211430 of 9051 papers

TitleStatusHype
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer TasksCode1
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution PerformanceCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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