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

Showing 33613370 of 9051 papers

TitleStatusHype
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation0
Diversity Preferences, Affirmative Action and Choice Rules0
Can Offline Metrics Measure Explanation Goals? A Comparative Survey Analysis of Offline Explanation Metrics in Recommender Systems0
Shortcuts for causal discovery of nonlinear models by score matching0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Controlled Randomness Improves the Performance of Transformer Models0
Knowledge Graph Context-Enhanced Diversified RecommendationCode0
Fundamental Limits of Membership Inference Attacks on Machine Learning Models0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
PSGText: Stroke-Guided Scene Text Editing with PSP Module0
Show:102550
← PrevPage 337 of 906Next →

No leaderboard results yet.