xie-lab-ml/deep-learning-dynamics-paper-list
This is a list of peer-reviewed representative papers on deep learning dynamics (optimization dynamics of neural networks). The success of deep learning attributes to both network architecture and stochastic optimization. Thus, deep learning dynamics play an essentially important role in theoretical foundation of deep learning.
This is a curated list of research papers focused on how deep learning models learn and optimize, particularly the 'dynamics' of their training process. It takes in recent, peer-reviewed academic papers related to deep learning optimization and outputs categorized lists with direct links to PDFs. This resource is for machine learning researchers, theoretical computer scientists, and academics interested in the fundamental mathematical underpinnings of deep neural networks.
294 stars. No commits in the last 6 months.
Use this if you are a researcher studying the theoretical aspects of deep learning optimization and need a focused collection of relevant literature.
Not ideal if you are a practitioner looking for practical implementation guides or tutorials on building deep learning models.
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Apr 10, 2024
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