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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 611620 of 9051 papers

TitleStatusHype
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Diverse and Admissible Trajectory Prediction through Multimodal Context UnderstandingCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
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