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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

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Showing 2401–2410 of 9051 papers

TitleStatusHype
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning—0
CLEVRER-Humans: Describing Physical and Causal Events the Human Way—0
CLEP-GAN: An Innovative Approach to Subject-Independent ECG Reconstruction from PPG Signals—0
Affinity and Diversity: A Unified Metric for Demonstration Selection via Internal Representations—0
CLearViD: Curriculum Learning for Video Description—0
Class label autoencoder for zero-shot learning—0
A Prospective-Performance Network to Alleviate Myopia in Beam Search for Response Generation—0
Designing Affine OCDM Systems with Maximum Diversity—0
A proposed new metric for the conceptual diversity of a text—0
Classifier Pool Generation based on a Two-level Diversity Approach—0
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