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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 5076–5100 of 9051 papers

TitleStatusHype
Role Play: Learning Adaptive Role-Specific Strategies in Multi-Agent Interactions—0
Roles of repertoire diversity in robustness of humoral immune response—0
RoomTour3D: Geometry-Aware Video-Instruction Tuning for Embodied Navigation—0
ROOTS: a toolkit for easy, fast and consistent processing of large sequential annotated data collections—0
Rope3D: The Roadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task—0
RopeBEV: A Multi-Camera Roadside Perception Network in Bird's-Eye-View—0
Rotational 3D Texture Classification Using Group Equivariant CNNs—0
Multi-Cycle Assignment Problems with Rotational Diversity—0
Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model—0
RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition—0
Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset—0
Ruby Teaming: Improving Quality Diversity Search with Memory for Automated Red Teaming—0
Toward best research practices in AI Psychology—0
Runtime Analysis of Evolutionary Diversity Optimization on the Multi-objective (LeadingOnes, TrailingZeros) Problem—0
Runtime Analysis of Probabilistic Crowding and Restricted Tournament Selection for Bimodal Optimisation—0
Runtime Analysis of Quality Diversity Algorithms—0
RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering—0
S3PT: Scene Semantics and Structure Guided Clustering to Boost Self-Supervised Pre-Training for Autonomous Driving—0
S^3-TTA: Scale-Style Selection for Test-Time Augmentation in Biomedical Image Segmentation—0
SA-Attack: Improving Adversarial Transferability of Vision-Language Pre-training Models via Self-Augmentation—0
SaFARi: State-Space Models for Frame-Agnostic Representation—0
Safe Perception -- A Hierarchical Monitor Approach—0
Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation—0
SALE-Based Offline Reinforcement Learning with Ensemble Q-Networks—0
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