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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 2801–2825 of 9051 papers

TitleStatusHype
Deep Reinforcement Learning for Dialogue GenerationCode0
Deep Reinforcement Learning-based Exploration of Web ApplicationsCode0
Barrier-Free Microhabitats: Self-Organized Seclusion in Microbial CommunitiesCode0
Diversity vs. Recognizability: Human-like generalization in one-shot generative modelsCode0
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimalityCode0
Diversity with Cooperation: Ensemble Methods for Few-Shot ClassificationCode0
Fairness and Diversity in Recommender Systems: A SurveyCode0
Fast Texture Synthesis via Pseudo OptimizerCode0
Facts That MatterCode0
Fair and Diverse DPP-based Data SummarizationCode0
Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on Fairness-Related QueriesCode0
Hypergraph Clustering for Finding Diverse and Experienced GroupsCode0
DeepPath: A Reinforcement Learning Method for Knowledge Graph ReasoningCode0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Face Manifold: Manifold Learning for Synthetic Face GenerationCode0
Facilitating bootstrapped and rarefaction-based microbiome diversity analysis with q2-bootsCode0
An adaptative differential evolution with enhanced diversity and restart mechanismCode0
Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?Code0
Expressivity of Parameterized and Data-driven Representations in Quality Diversity SearchCode0
BAL: Balancing Diversity and Novelty for Active LearningCode0
DLCRec: A Novel Approach for Managing Diversity in LLM-Based Recommender SystemsCode0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
DMix: Adaptive Distance-aware Interpolative MixupCode0
Exploring the Performance-Reproducibility Trade-off in Quality-DiversityCode0
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