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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 23512360 of 9051 papers

TitleStatusHype
Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial CurvesCode0
Generating Sentential Arguments from Diverse Perspectives on Controversial TopicCode0
BOLD5000: A public fMRI dataset of 5000 imagesCode0
A Diversity-Promoting Objective Function for Neural Conversation ModelsCode0
Generating Neural Networks with Neural NetworksCode0
Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture SynthesisCode0
Block Flow: Learning Straight Flow on Data BlocksCode0
Generating Informative and Diverse Conversational Responses via Adversarial Information MaximizationCode0
Generating Diverse Descriptions from Semantic GraphsCode0
A Diversity-Enhanced Knowledge Distillation Model for Practical Math Word Problem SolvingCode0
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