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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 35913600 of 9051 papers

TitleStatusHype
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection0
Fast ABC with joint generative modelling and subset simulation0
Fast Adaptation in Generative Models with Generative Matching Networks0
Ask to Understand: Question Generation for Multi-hop Question Answering0
Fast and High Quality Highlight Removal from A Single Image0
A Holistic Evaluation of Piano Sound Quality0
Fast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments0
Active Learning for Lane Detection: A Knowledge Distillation Approach0
Exploring the Law of Numbers: Evidence from China's Real Estate0
Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality prediction0
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