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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 3051–3075 of 9051 papers

TitleStatusHype
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion—0
WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data—0
Computational detection of antigen specific B cell receptors following immunizationCode0
Understanding and Estimating Domain Complexity Across Domains—0
Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)—0
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion—0
MotionScript: Natural Language Descriptions for Expressive 3D Human Motions—0
Point Cloud Part Editing: Segmentation, Generation, Assembly, and SelectionCode1
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem—0
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAXCode2
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Density Descent for Diversity Optimization—0
A Multimodal Approach for Advanced Pest Detection and Classification—0
Few-Shot Learning from Augmented Label-Uncertain Queries in Bongard-HOI—0
Sails and Anchors: The Complementarity of Exploratory and Exploitative Scientists in Knowledge Creation—0
Rethinking Robustness of Model AttributionsCode0
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning—0
Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing dataCode0
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data AugmentationCode1
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
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