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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 471480 of 9051 papers

TitleStatusHype
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source DataCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
Diversity-based Trajectory and Goal Selection with Hindsight Experience ReplayCode1
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
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