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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 431440 of 9051 papers

TitleStatusHype
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response GenerationCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Differentiable Quality DiversityCode1
Active learning for medical image segmentation with stochastic batchesCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
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