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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 311320 of 9051 papers

TitleStatusHype
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Curriculum-guided Hindsight Experience ReplayCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Adaptable Agent Populations via a Generative Model of PoliciesCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
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