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Found 6 papers, 3 papers with code
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.
Precise Energy Consumption Measurements of Heterogeneous Artificial Intelligence Workloads
Therefore, accurate measurements of the power draw of AI workflows on different types of compute nodes is key to algorithmic improvements and the design of future compute clusters and hardware.
Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)
With increasing data and model complexities, the time required to train neural networks has become prohibitively large.
HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics
With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis.
