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Exploration of Efficient Model Updates: Dynamic Sparse Training for Continual Learning

Exploration of Efficient Model Updates: Dynamic Sparse Training for Continual Learning

by instadatahelp | Aug 30, 2023 | AI Blogs

Continual learning (CL) is the ability of an intelligent system to acquire and retain knowledge from a stream of data with minimal computational overhead. Various approaches, such as regularization, replay, architecture, and parameter isolation, have been introduced...

Optimizing Online Operations on Riemannian Manifolds

by instadatahelp | Aug 30, 2023 | AI Blogs

Online Optimization over Riemannian Manifolds Authors: Xi Wang, Zhipeng Tu, Yiguang Hong, Yingyi Wu, Guodong Shi; Volume 24, Issue 84, Pages 1-67, 2023. Abstract In recent years, there has been a significant increase in research on online optimization. This paper...

Examining the Impact of Pre-training on Lifelong Learning: An Empirical Study

by instadatahelp | Aug 30, 2023 | AI Blogs

Investigating the Role of Pre-training in Lifelong Learning: An Empirical Study Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell; 24(214):1−50, 2023. Abstract The lifelong learning paradigm in machine learning is an appealing alternative to isolated...
Exploration of Efficient Model Updates: Dynamic Sparse Training for Continual Learning

Statistical Verification with Imprecise Neural Networks: An Approach for Distributional Robustness

by instadatahelp | Aug 30, 2023 | AI Blogs

The arXivLabs framework enables collaborators to develop and share new features directly on our website. Both individuals and organizations that collaborate with arXivLabs uphold our values of openness, community, excellence, and user data privacy. We are dedicated to...

Dimension-Based Mixed Membership Models for Multivariate Categorical Data

by instadatahelp | Aug 30, 2023 | AI Blogs

Dimension-Grouped Mixed Membership Models for Multivariate Categorical Data Authors: Yuqi Gu, Elena E. Erosheva, Gongjun Xu, David B. Dunson; Volume 24, Issue 88, Pages 1-49, 2023. Abstract Mixed Membership Models (MMMs) are widely used latent structure models for...
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