Abstract page for arXiv paper 2409.11290v1: Neural Networks for Vehicle Routing Problem

Learn how to implement regularization techniques to boost performances and prevent Neural Network overfitting

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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Deep neural networks will allow signal transfer of nerve cells to be analyzed in real time in the future. That is the result of a study conducted by physiologists at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) that has been published in the journal Communications Chemistry. The method is not only of relevance for neuromedicine, it can also be used for investigating chemical reactions

Using an MDL-based cost function with neural networks

Generalized dynamic feedforward networks using differential equations

Generalized dynamic feedforward networks using differential equations

Machine learning, neural networks, computer vision, NLP models and architectures for developers. Data science and artificial intelligence news

Gustav's blog Random things, mostly about technical stuff Menu Skip to content Neural Networks using Pylearn2 – termination criteria, momentum and learning rate adjustment 2 Replies A while ago I wrote a post describing how to use Pylearn2 for training neural networks. By my very modest standards it became quite popular so I thought I should follow it with a more advanced example that introduces more advanced termination criteria, momentum and learning rate adjustment. It should be noted that this post is