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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 23812390 of 9051 papers

TitleStatusHype
AAMDM: Accelerated Auto-regressive Motion Diffusion Model0
Diversity-aware Multi-Video Summarization0
Diversity-Aware Policy Optimization for Large Language Model Reasoning0
Diversity-based Design Assist for Large Legged Robots0
ClosNets: a Priori Sparse Topologies for Faster DNN Training0
Closed-Loop Memory GAN for Continual Learning0
AraSAS: The Open Source Arabic Semantic Tagger0
Affinity-Preserving Random Walk for Multi-Document Summarization0
A Randomized Link Transformer for Diverse Open-Domain Dialogue Generation0
Cross Pseudo Supervision Framework for Sparsely Labelled Geospatial Images0
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