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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 2901–2925 of 9051 papers

TitleStatusHype
Bags of Projected Nearest Neighbours: Competitors to Random Forests?Code0
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
Particle Smoothing Variational ObjectivesCode0
Deep Ensembles Work, But Are They Necessary?Code0
Exploring Model Learning Heterogeneity for Boosting Ensemble RobustnessCode0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual LearningCode0
One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversityCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
First the worst: Finding better gender translations during beam searchCode0
Deep Ensembles with Hierarchical Diversity PruningCode0
Exploring Flat Minima for Domain Generalization with Large Learning RatesCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Adversarial Multi-lingual Neural Relation ExtractionCode0
Exploring Diversity in Back Translation for Low-Resource Machine TranslationCode0
Exploring Format Consistency for Instruction TuningCode0
A Workbench for Autograding Retrieve/Generate SystemsCode0
Deep Co-Training for Semi-Supervised Image SegmentationCode0
Exploiting ConvNet Diversity for Flooding IdentificationCode0
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist ExaminationCode0
Explaining crime diversity with Google street viewCode0
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial DiversityCode0
Exploratory State Representation LearningCode0
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution StrategiesCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
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