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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 4126–4150 of 9051 papers

TitleStatusHype
Natural Averaging May Complement Known Biological Constraints in Bi-parental Reproduction's Advantages Over Mono-parental in Conserving Species Quantitative Traits—0
Set Prediction Guided by Semantic Concepts for Diverse Video Captioning—0
Revisiting Knowledge Distillation under Distribution ShiftCode0
Diversity-Based Recruitment in Crowdsensing By Combinatorial Multi-Armed Bandits—0
Learning from diversity: jati fractionalization, social expectations and improved sanitation practices in India—0
Efficacy of Machine-Generated Instructions—0
TimePillars: Temporally-Recurrent 3D LiDAR Object Detection—0
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion—0
WellFactor: Patient Profiling using Integrative Embedding of Healthcare Data—0
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
Computational detection of antigen specific B cell receptors following immunizationCode0
Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)—0
Understanding and Estimating Domain Complexity Across Domains—0
AMD:Anatomical Motion Diffusion with Interpretable Motion Decomposition and Fusion—0
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning—0
MotionScript: Natural Language Descriptions for Expressive 3D Human Motions—0
New Classes of the Greedy-Applicable Arm Feature Distributions in the Sparse Linear Bandit Problem—0
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Domain adaption and physical constrains transfer learning for shale gas production—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
K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning—0
Rethinking Robustness of Model AttributionsCode0
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