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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 2926–2950 of 9051 papers

TitleStatusHype
Multi-Agent Diagnostics for Robustness via Illuminated Diversity—0
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size—0
Benchmarking the Fairness of Image Upsampling MethodsCode0
ARGS: Alignment as Reward-Guided SearchCode1
UniHDA: A Unified and Versatile Framework for Multi-Modal Hybrid Domain Adaptation—0
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup IdentificationCode0
Local Diversity of Condorcet DomainsCode0
SuperCLUE-Math6: Graded Multi-Step Math Reasoning Benchmark for LLMs in ChineseCode2
An Improved Grey Wolf Optimization Algorithm for Heart Disease Prediction—0
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders—0
Enhancing Recommendation Diversity by Re-ranking with Large Language Models—0
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Large-scale Reinforcement Learning for Diffusion Models—0
Navigating the Thin Line: Examining User Behavior in Search to Detect Engagement and Backfire Effects—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
Interactive Mars Image Content-Based Search with Interpretable Machine Learning—0
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic RoomsCode2
Quality-Diversity Algorithms Can Provably Be Helpful for Optimization—0
Learning High-Quality and General-Purpose Phrase RepresentationsCode1
QoS-Aware 3D Coverage Deployment of UAVs for Internet of Vehicles in Intelligent Transportation—0
Divide and not forget: Ensemble of selectively trained experts in Continual LearningCode0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
BPDO:Boundary Points Dynamic Optimization for Arbitrary Shape Scene Text Detection—0
Generalized Face Liveness Detection via De-fake Face Generator—0
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