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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 4531–4540 of 9051 papers

TitleStatusHype
Self-Supervised Correspondence Estimation via Multiview RegistrationCode1
Improved Beam Search for Hallucination Mitigation in Abstractive Summarization—0
State Space Closure: Revisiting Endless Online Level Generation via Reinforcement LearningCode0
Federated Neural Topic ModelsCode0
On the effectiveness of partial variance reduction in federated learning with heterogeneous dataCode1
HierarchyFL: Heterogeneous Federated Learning via Hierarchical Self-Distillation—0
Towards Generating Diverse Audio Captions via Adversarial Training—0
Brain Tumor Synthetic Data Generation with Adaptive StyleGANsCode0
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
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