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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 321330 of 9051 papers

TitleStatusHype
Curriculum-guided Hindsight Experience ReplayCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Dataset Factorization for CondensationCode1
A Case for Rejection in Low Resource ML DeploymentCode1
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