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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 6451–6475 of 9051 papers

TitleStatusHype
Learned Region Sparsity and Diversity Also Predicts Visual Attention—0
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations—0
Learning 3D Semantic Segmentation with only 2D Image Supervision—0
Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration—0
Learning an evolved mixture model for task-free continual learning—0
Learning a Non-Redundant Collection of Classifiers—0
Automated Data Augmentation for Few-Shot Time Series Forecasting: A Reinforcement Learning Approach Guided by a Model Zoo—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Learning Camera Movement Control from Real-World Drone Videos—0
Learning Canonical Transformations—0
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification—0
Learning Collective Action under Risk Diversity—0
Learning Compact Reward for Image Captioning—0
Learning Continually by Spectral Regularization—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Learning Debiased and Disentangled Representations for Semantic Segmentation—0
Learning Deep Features for Scene Recognition using Places Database—0
Learning Determinantal Point Processes by Corrective Negative Sampling—0
Learning Disentangled Representations for Image Translation—0
Learning Diverse Generations using Determinantal Point Processes—0
Learning Diverse Policies in MOBA Games via Macro-Goals—0
Learning Diverse Policies with Soft Self-Generated Guidance—0
Learning diverse rankings with multi-armed bandits—0
Learning Diverse Representations for Fast Adaptation to Distribution Shift—0
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