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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 15511560 of 9051 papers

TitleStatusHype
Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword SpottingCode1
Rainbow Memory: Continual Learning with a Memory of Diverse SamplesCode1
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized ImagesCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic EntropyCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Red Teaming Language Models with Language ModelsCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
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