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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 7951–7975 of 9051 papers

TitleStatusHype
Dance Generation by Sound Symbolic Words—0
Corporate Social Responsibility and Corporate Governance: A cognitive approach—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning—0
Adversarial Text-to-Image Synthesis: A Review—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
Coreset Selection for Object Detection—0
DarkBERT: A Language Model for the Dark Side of the Internet—0
DARLEI: Deep Accelerated Reinforcement Learning with Evolutionary Intelligence—0
Coreference in Spoken vs. Written Texts: a Corpus-based Analysis—0
DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification—0
Coreference Chains Categorization by Sequence Clustering—0
CORD: Generalizable Cooperation via Role Diversity—0
Data Augmentation by Concatenation for Low-Resource Translation: A Mystery and a Solution—0
Data Augmentation by Selecting Mixed Classes Considering Distance Between Classes—0
Data Augmentation for Image Classification using Generative AI—0
Data Augmentation for Leaf Segmentation and Counting Tasks in Rosette Plants—0
Data Augmentation for Neural Machine Translation using Generative Language Model—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Data Augmentation for Seizure Prediction with Generative Diffusion Model—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
Data Augmentation in Earth Observation: A Diffusion Model Approach—0
Copy-Paste Image Augmentation with Poisson Image Editing for Ultrasound Instance Segmentation Learning—0
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling—0
Data Augmentation with Adversarial Training for Cross-Lingual NLI—0
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