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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 5451–5475 of 9051 papers

TitleStatusHype
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Techtile -- Open 6G R&D Testbed for Communication, Positioning, Sensing, WPT and Federated Learning—0
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery—0
MOST-Net: A Memory Oriented Style Transfer Network for Face Sketch Synthesis—0
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillationCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Building Synthetic Speaker Profiles in Text-to-Speech Systems—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
MAML and ANIL Provably Learn Representations—0
Multi-Objective Quality Diversity OptimizationCode0
Red Teaming Language Models with Language ModelsCode1
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
A Coalition Formation Game Approach for Personalized Federated Learning—0
Non-Coherent MIMO-OFDM Uplink empowered by the Spatial Diversity in Reflecting Surfaces—0
Quality-diversity for aesthetic evolution—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
TIML: Task-Informed Meta-Learning for AgricultureCode1
Privacy-Aware Crowd Labelling for Machine Learning Tasks—0
Spatial Diversity in Radar Detection via Active Reconfigurable Intelligent Surfaces—0
Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer—0
Accelerated Quality-Diversity through Massive ParallelismCode2
Lipschitz-constrained Unsupervised Skill DiscoveryCode1
Improving Screening Processes via Calibrated Subset SelectionCode0
Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness—0
CIC: Contrastive Intrinsic Control for Unsupervised Skill DiscoveryCode1
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