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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

Showing 44614470 of 9051 papers

TitleStatusHype
InsectMamba: Insect Pest Classification with State Space Model0
Intelligent Reflecting Surfaces assisted Laser-based Optical Wireless Communication Networks0
Iterative Averaging in the Quest for Best Test Error0
Behaviour Planning: A Toolkit for Diverse Planning0
DesnowNet: Context-Aware Deep Network for Snow Removal0
Behavioural Repertoire via Generative Adversarial Policy Networks0
Market Design with Distributional Objectives0
A database for face presentation attack using wax figure faces0
Designing Recommender Systems to Depolarize0
Designing Data: Proactive Data Collection and Iteration for Machine Learning0
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