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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 30413050 of 9051 papers

TitleStatusHype
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating AttentionCode0
AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series ForecastingCode0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
ETS: Efficient Tree Search for Inference-Time ScalingCode0
Ethical Considerations for Responsible Data CurationCode0
EuLearn: A 3D database for learning Euler characteristicsCode0
Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of MetricsCode0
A mirror-Unet architecture for PET/CT lesion segmentationCode0
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials DataCode0
Data Fusion for Deep Learning on Transport Mode Detection: A Case StudyCode0
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