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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 22012210 of 9051 papers

TitleStatusHype
Adversarial Inference for Multi-Sentence Video DescriptionCode0
CausalDialogue: Modeling Utterance-level Causality in ConversationsCode0
Graph-guided Architecture Search for Real-time Semantic SegmentationCode0
CatVRNN: Generating Category Texts via Multi-task LearningCode0
Adversarial Imitation Learning with Trajectorial Augmentation and CorrectionCode0
Cats or CAT scans: transfer learning from natural or medical image source datasets?Code0
CatGAN: Category-aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text GenerationCode0
Gram-Elites: N-Gram Based Quality-Diversity SearchCode0
Group Relative Policy Optimization for Image CaptioningCode0
CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and CalibrationCode0
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