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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 16411650 of 9051 papers

TitleStatusHype
Integrating Present and Past in Unsupervised Continual LearningCode0
Instrumental Variable Estimation for Compositional TreatmentsCode0
Intent Factored Generation: Unleashing the Diversity in Your Language ModelCode0
Interactive Neural Style Transfer with ArtistsCode0
Investigating Evaluation of Open-Domain Dialogue Systems With Human Generated Multiple ReferencesCode0
InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content DetectionCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
Auditing for Diversity using Representative ExamplesCode0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
INSightR-Net: Interpretable Neural Network for Regression using Similarity-based Comparisons to Prototypical ExamplesCode0
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