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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 16811690 of 9051 papers

TitleStatusHype
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
A Culturally-Aware Tool for Crowdworkers: Leveraging Chronemics to Support Diverse Work StylesCode0
Insect Identification in the Wild: The AMI DatasetCode0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
Input-gradient space particle inference for neural network ensemblesCode0
Information-Theoretic Active Learning for Content-Based Image RetrievalCode0
INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced ClassificationCode0
Intrinsically-Motivated Humans and Agents in Open-World ExplorationCode0
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