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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 17911800 of 9051 papers

TitleStatusHype
A study of quality and diversity in K+1 GANs0
AIDE: Task-Specific Fine Tuning with Attribute Guided Multi-Hop Data Expansion0
Active Learning-Guided Seq2Seq Variational Autoencoder for Multi-target Inhibitor Generation0
Content-adaptive Representation Learning for Fast Image Super-resolution0
Construction of Responsive Utterance Corpus for Attentive Listening Response Production0
A study of conceptual language similarity: comparison and evaluation0
Construction and optimization of health behavior prediction model for the elderly in smart elderly care0
Constructing Enhanced Mutual Information for Online Class-Incremental Learning0
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations0
AID++: An Updated Version of AID on Scene Classification0
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