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Found 4 papers, 2 papers with code
AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning
Communication bottlenecks hinder the scalability of distributed neural network training, particularly on distributed-memory computing clusters.
Harnessing Orthogonality to Train Low-Rank Neural Networks
This study explores the learning dynamics of neural networks by analyzing the singular value decomposition (SVD) of their weights throughout training.
Feed-Forward Optimization With Delayed Feedback for Neural Networks
Backpropagation has long been criticized for being biologically implausible, relying on concepts that are not viable in natural learning processes.
Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations
We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search.
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