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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
Fast Texture Synthesis via Pseudo OptimizerCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Federated Neural Topic ModelsCode0
Fast and Practical Neural Architecture SearchCode0
Fast and Functional Structured Data Generators Rooted in Out-of-Equilibrium PhysicsCode0
Deep Surrogate Assisted MAP-Elites for Automated Hearthstone DeckbuildingCode0
Batched Large-scale Bayesian Optimization in High-dimensional SpacesCode0
Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary EvaluationCode0
Farsighted Probabilistic Sampling: A General Strategy for Boosting Local Search MaxSAT SolversCode0
FASTdoop: a versatile and efficient library for the input of FASTA and FASTQ files for MapReduce Hadoop bioinformatics applicationsCode0
Federated Stain Normalization for Computational PathologyCode0
FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAGCode0
An Open-World, Diverse, Cross-Spatial-Temporal Benchmark for Dynamic Wild Person Re-IdentificationCode0
Diversity matters: Robustness of bias measurements in WikidataCode0
Batch Decorrelation for Active Metric LearningCode0
FairER: Entity Resolution with Fairness ConstraintsCode0
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
Facts That MatterCode0
Batch Active Learning Using Determinantal Point ProcessesCode0
Diversity of emergent dynamics in competitive threshold-linear networksCode0
Fair and Diverse DPP-based Data SummarizationCode0
Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-bootsCode0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Batch Active Learning at ScaleCode0
Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related QueriesCode0
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