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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 83268350 of 9051 papers

TitleStatusHype
Metropolis Theorem and Its Applications in Single Image Detail EnhancementCode0
CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forumsCode0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
Quality and Diversity Optimization: A Unifying Modular FrameworkCode0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape RecoveryCode0
GDPP: Learning Diverse Generations Using Determinantal Point ProcessCode0
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided ParaphrasingCode0
mHuBERT-147: A Compact Multilingual HuBERT ModelCode0
mHumanEval -- A Multilingual Benchmark to Evaluate Large Language Models for Code GenerationCode0
Diversifying Semantic Image Synthesis and Editing via Class- and Layer-wise VAEsCode0
Exclusion of the fittest predicts microbial community diversity in fluctuating environmentsCode0
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsCode0
Combining Predictions under Uncertainty: The Case of Random Decision TreesCode0
Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene ReconstructionCode0
AROID: Improving Adversarial Robustness Through Online Instance-Wise Data AugmentationCode0
Diversifying Dialogue Generation with Non-Conversational TextCode0
Microphone Conversion: Mitigating Device Variability in Sound Event ClassificationCode0
A Revisit of Sparse Coding Based Anomaly Detection in Stacked RNN FrameworkCode0
Solving the apparent diversity-accuracy dilemma of recommender systemsCode0
MIGS: Meta Image Generation from Scene GraphsCode0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
MINT -- Mainstream and Independent News Text CorpusCode0
Mind the GAP: A Balanced Corpus of Gendered Ambiguous PronounsCode0
Diversifying Deep Ensembles: A Saliency Map Approach for Enhanced OOD Detection, Calibration, and AccuracyCode0
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