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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 10711080 of 9051 papers

TitleStatusHype
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
Implicit Neural Representations for Variable Length Human Motion GenerationCode1
Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence LearningCode1
Training-free Transformer Architecture SearchCode1
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake DetectionCode1
Quality Controlled Paraphrase GenerationCode1
Learning Affordance Grounding from Exocentric ImagesCode1
MotionAug: Augmentation with Physical Correction for Human Motion PredictionCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Complex Evolutional Pattern Learning for Temporal Knowledge Graph ReasoningCode1
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