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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 711720 of 9051 papers

TitleStatusHype
Multimodal Question Answering for Unified Information ExtractionCode1
Sieve: Multimodal Dataset Pruning Using Image Captioning ModelsCode1
Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-IdentificationCode1
KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse TransformationCode1
Structure Invariant Transformation for better Adversarial TransferabilityCode1
Dual Feature Augmentation Network for Generalized Zero-shot LearningCode1
Multi-Static ISAC in Cell-Free Massive MIMO: Precoder Design and Privacy AssessmentCode1
Euler-Lagrange Analysis of Generative Adversarial NetworksCode1
Design of Chain-of-Thought in Math Problem SolvingCode1
NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource LanguagesCode1
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