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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 6301–6325 of 9051 papers

TitleStatusHype
IRS-Assisted Massive MIMO-NOMA Networks: Exploiting Wave Polarization—0
IRS-Assisted Massive MIMO-NOMA Networks with Polarization Diversity—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI—0
Is Out-of-Distribution Detection Learnable?—0
ISP-Agnostic Image Reconstruction for Under-Display Cameras—0
Is Pre-training Truly Better Than Meta-Learning?—0
Is Support Set Diversity Necessary for Meta-Learning?—0
Is the Capability approach a useful tool for decision aiding in public policy making?—0
Is There Mode Collapse? A Case Study on Face Generation and Its Black-box Calibration—0
Is the U.S. Legal System Ready for AI's Challenges to Human Values?—0
Item-Item Music Recommendations With Side Information—0
Iterative Averaging in the Quest for Best Test Error—0
Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search—0
Iterative Classroom Teaching—0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Itinerary-aware Personalized Deep Matching at Fliggy—0
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry—0
Jailbreak Distillation: Renewable Safety Benchmarking—0
Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data—0
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling—0
Joint BS Selection, User Association, and Beamforming Design for Network Integrated Sensing and Communication—0
Joint Event Detection and Entity Resolution: a Virtuous Cycle—0
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