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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 13211330 of 9051 papers

TitleStatusHype
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed ClassificationCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Syn-to-Real GeneralizationCode1
Improve Student's Reasoning Generalizability through Cascading Decomposed CoTs DistillationCode1
Vision Transformers with Patch DiversificationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Improving Diversity with Adversarially Learned Transformations for Domain GeneralizationCode1
Improving Geo-diversity of Generated Images with Contextualized Vendi Score GuidanceCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
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