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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 621630 of 9051 papers

TitleStatusHype
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
An Informative Tracking BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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