SOTAVerified

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 4776–4800 of 9051 papers

TitleStatusHype
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
What are you optimizing for? Aligning Recommender Systems with Human Values—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
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions—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
Stochastic Region Pooling: Make Attention More Expressive—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Itinerary-aware Personalized Deep Matching at Fliggy—0
Beyond Diagonal RIS-Aided Networks: Performance Analysis and Sectorization Tradeoff—0
JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry—0
Towards a Universal Features Set for IoT Botnet Attacks Detection—0
Jailbreak Distillation: Renewable Safety Benchmarking—0
Beyond Conservatism: Diffusion Policies in Offline Multi-agent Reinforcement Learning—0
Towards Automatic Construction of Diverse, High-quality Image Dataset—0
Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data—0
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