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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 6501–6525 of 9051 papers

TitleStatusHype
Learning more with the same effort: how randomization improves the robustness of a robotic deep reinforcement learning agent—0
Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation—0
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Learning on Bandwidth Constrained Multi-Source Data with MIMO-inspired DPP MAP Inference—0
Learning Online from Corrective Feedback: A Meta-Algorithm for Robotics—0
Learning Personalized Alignment for Evaluating Open-ended Text Generation—0
Learning Policies for Contextual Submodular Prediction—0
Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing—0
Learning Quadruped Locomotion Policies using Logical Rules—0
Learning Semantic Segmentation from Multiple Datasets with Label Shifts—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data—0
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process—0
Learning the Designer's Preferences to Drive Evolution—0
Learning the Parameters of Determinantal Point Process Kernels—0
Learning to Act through Evolution of Neural Diversity in Random Neural Networks—0
Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulation—0
Learning to detect cloud and snow in remote sensing images from noisy labels—0
Learning to Detect Vehicles by Clustering Appearance Patterns—0
Learning to Diversify for Product Question Generation—0
Learning to Diversify Neural Text Generation via Degenerative Model—0
Learning to Diversify via Weighted Kernels for Classifier Ensemble—0
Learning to exploit z-Spatial Diversity for Coherent Nonlinear Optical Fiber Communication—0
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research—0
Learning to Generate Videos Using Neural Uncertainty Priors—0
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