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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 8201–8225 of 9051 papers

TitleStatusHype
GenZSL: Generative Zero-Shot Learning Via Inductive Variational AutoencoderCode0
Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity ConstraintsCode0
The Curious Decline of Linguistic Diversity: Training Language Models on Synthetic TextCode0
Diversity matters: Robustness of bias measurements in WikidataCode0
Promoting Counterfactual Robustness through DiversityCode0
The Curse of Diversity in Ensemble-Based ExplorationCode0
Promoting Fairness and Diversity in Speech Datasets for Mental Health and Neurological Disorders ResearchCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
Genetic Algorithm with Innovative Chromosome Patterns in the Breeding ProcessCode0
Diversity in Fashion Recommendation using Semantic ParsingCode0
SkinAugment: Auto-Encoding Speaker Conversions for Automatic Speech TranslationCode0
Generative Monoculture in Large Language ModelsCode0
Generative AI and Creativity: A Systematic Literature Review and Meta-AnalysisCode0
A comprehensive representation of selection at loci with multiple alleles that allows complex forms of genotypic fitnessCode0
All-In-One Underwater Image Enhancement using Domain-Adversarial LearningCode0
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental LearningCode0
SliderSpace: Decomposing the Visual Capabilities of Diffusion ModelsCode0
Diversity-Enriched Option-CriticCode0
Adversarial Multi-lingual Neural Relation ExtractionCode0
Alleviating the Long-Tail Problem in Conversational Recommender SystemsCode0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
Complex Locomotion Skill Learning via Differentiable PhysicsCode0
A Simple Yet Effective Approach for Diversified Session-Based RecommendationCode0
SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity RepresentationCode0
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