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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
Improving the Evaluation of Generative Models with Fuzzy LogicCode0
A Hierarchical Deep Learning Approach for Minority Instrument DetectionCode0
Improving the Data Efficiency of Multi-Objective Quality-Diversity through Gradient Assistance and Crowding ExplorationCode0
A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddingsCode0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
A Simple, Fast Diverse Decoding Algorithm for Neural GenerationCode0
To Ensemble or Not Ensemble: When does End-To-End Training Fail?Code0
JoTR: A Joint Transformer and Reinforcement Learning Framework for Dialog Policy LearningCode0
Computational detection of antigen specific B cell receptors following immunizationCode0
Improving Screening Processes via Calibrated Subset SelectionCode0
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