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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 451460 of 9051 papers

TitleStatusHype
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
Self-Supervision Improves Diffusion Models for Tabular Data ImputationCode1
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial OptimizationCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph GenerationCode1
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealingCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
Personalized Privacy Protection Mask Against Unauthorized Facial RecognitionCode1
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
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