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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

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Showing 2751–2775 of 9051 papers

TitleStatusHype
Embedding Cultural Diversity in Prototype-based Recommender Systems—0
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software—0
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection—0
Diversity in immunogenomics: the value and the challenge—0
Diversity in Kemeny Rank Aggregation: A Parameterized Approach—0
Diversity in Machine Learning—0
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity in Spectral Learning for Natural Language Parsing—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Diversity in the radiation-induced transcriptomic temporal response of mouse brain tissue regions—0
Diversity driven Query Rewriting in Search Advertising—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations—0
Diversity Matters When Learning From Ensembles—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
Burn After Reading: Online Adaptation for Cross-domain Streaming Data—0
Diversity Measurement and Subset Selection for Instruction Tuning Datasets—0
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning—0
A comparative study of universal quantum computing models: towards a physical unification—0
Diversity of Ensembles for Data Stream Classification—0
Diversity of growth rates maximizes phytoplankton productivity in an eddying ocean—0
Diversity of immune strategies explained by adaptation to pathogen statistics—0
Diversity of meso-scale architecture in human and non-human connectomes—0
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
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