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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 3476–3500 of 9051 papers

TitleStatusHype
Constraint-based Diversification of JOP Gadgets—0
Assessment of Practical Smart Gateway Diversity Based on Multi-Site Measurements in Q/V band—0
AID++: An Updated Version of AID on Scene Classification—0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation—0
Abnormal Event Detection In Videos Using Deep Embedding—0
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code—0
Embracing Diversity: A Multi-Perspective Approach with Soft Labels—0
Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks—0
Constrained Pseudo-market Equilibrium—0
Constrained Interacting Submodular Groupings—0
Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle—0
Consistent Multiple Sequence Decoding—0
Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics—0
AI Competitions and Benchmarks: Competition platforms—0
Consistent Flow Distillation for Text-to-3D Generation—0
Consistency and Diversity induced Human Motion Segmentation—0
Assessing the Coherence Modeling Capabilities of Pretrained Transformer-based Language Models—0
AIBench Scenario: Scenario-distilling AI Benchmarking—0
Assessing Social Determinants-Related Performance Bias of Machine Learning Models: A case of Hyperchloremia Prediction in ICU Population—0
Considerations for Ethical Speech Recognition Datasets—0
Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?—0
AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis—0
Conservative Exploration using Interleaving—0
Consecutive Question Generation with Multitask Joint Reranking and Dynamic Rationale Search—0
Assessing Quality-Diversity Neuro-Evolution Algorithms Performance in Hard Exploration Problems—0
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