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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 2726–2750 of 9051 papers

TitleStatusHype
Anomaly Detection in Video Sequence With Appearance-Motion Correspondence—0
Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity—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
Advances in Robust Federated Learning: Heterogeneity Considerations—0
Diversity-Driven Selection of Exploration Strategies in Multi-Armed Bandits—0
Diversity Enhanced Table-to-Text Generation via Type Control—0
Diversity Enhancement for Micro-Differential Evolution—0
Diversity Enhancement via Magnitude—0
Diversity Enhances an LLM's Performance in RAG and Long-context Task—0
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation—0
CADSpotting: Robust Panoptic Symbol Spotting on Large-Scale CAD Drawings—0
Diversity-Fair Online Selection—0
Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer—0
Diversity from Human Feedback—0
Diversity-grounded Channel Prototypical Learning for Out-of-Distribution Intent Detection—0
Diversity driven Query Rewriting in Search Advertising—0
Efficient Scene Recovery Using Luminous Flux Prior—0
Diversity Handling In Evolutionary Landscape—0
Diversity Helps Jailbreak Large Language Models—0
Diversity improves performance in excitable networks—0
Diversity in Action: General-Sum Multi-Agent Continuous Inverse Optimal Control—0
Diversity in Choice as Majorization—0
Diversity-Driven Learning: Tackling Spurious Correlations and Data Heterogeneity in Federated Models—0
Diversity-Driven Generative Dataset Distillation Based on Diffusion Model with Self-Adaptive Memory—0
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