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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

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Showing 1981–1990 of 9051 papers

TitleStatusHype
Advances in Diffusion Models for Image Data Augmentation: A Review of Methods, Models, Evaluation Metrics and Future Research Directions—0
On the Effectiveness of Acoustic BPE in Decoder-Only TTS—0
RobocupGym: A challenging continuous control benchmark in RobocupCode1
Regurgitative Training: The Value of Real Data in Training Large Language Models—0
Advanced Framework for Animal Sound Classification With Features Optimization—0
Semantically Rich Local Dataset Generation for Explainable AI in GenomicsCode0
Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning—0
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking DatasetCode1
Towards a Scalable Reference-Free Evaluation of Generative ModelsCode0
Anti-Collapse Loss for Deep Metric Learning Based on Coding Rate MetricCode0
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