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 19611970 of 9051 papers

TitleStatusHype
Augmented Data Science: Towards Industrialization and Democratization of Data Science0
Crowded trades, market clustering, and price instability0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout0
Active Learning for Object Detection with Non-Redundant Informative Sampling0
Augmented Message Passing Stein Variational Gradient Descent0
Crowdsourcing Diverse Paraphrases for Training Task-oriented Bots0
Condorcet's Jury Theorem for Consensus Clustering and its Implications for Diversity0
Crowdsourcing Lexical Diversity0
Crowdsourcing Multiple Choice Science Questions0
Condition-Transforming Variational AutoEncoder for Conversation Response Generation0
Show:102550
← PrevPage 197 of 906Next →

No leaderboard results yet.