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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 3676–3700 of 9051 papers

TitleStatusHype
Controllable and Diverse Text Generation in E-commerce—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
Few-shot Image Generation via Style Adaptation and Content Preservation—0
Few-shot Image Generation with Elastic Weight Consolidation—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs—0
Few-Shot Learning with Intra-Class Knowledge Transfer—0
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
A document processing pipeline for the construction of a dataset for topic modeling based on the judgments of the Italian Supreme Court—0
Controllable Exploration of a Design Space via Interactive Quality Diversity—0
Diverse Trajectory Forecasting with Determinantal Point Processes—0
Diverse, Top-k, and Top-Quality Planning Over Simulators—0
AIR: Unifying Individual and Collective Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Diverse Single Image Generation with Controllable Global Structure—0
Few-Step Diffusion via Score identity Distillation—0
FFF: Fixing Flawed Foundations in contrastive pre-training results in very strong Vision-Language models—0
Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning—0
An Improved Dung Beetle Optimizer for Random Forest Optimization—0
A collaborative decomposition-based evolutionary algorithm integrating normal and penalty-based boundary intersection for many-objective optimization—0
Generating Discriminative Object Proposals via Submodular Ranking—0
Diverse Shape Completion via Style Modulated Generative Adversarial Networks—0
Diverse Score Distillation—0
Natural Averaging May Complement Known Biological Constraints in Bi-parental Reproduction's Advantages Over Mono-parental in Conserving Species Quantitative Traits—0
A Niching Indicator-Based Multi-modal Many-objective Optimizer—0
Generate, Evaluate, and Select: A Dialogue System with a Response Evaluator for Diversity-Aware Response Generation—0
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