A neural processing unit (NPU) is a specialized computer microprocessor designed to mimic the processing function of the human brain
A solid neural network training guide is worth more than any single framework tutorial, because the core concepts—weight initialisation, loss functions
I started working though the second chapter of McCaffrey’s book Neural Networks Using C# Succinctly to see if I could write the examples using F#. McCaffrey’s code is tough to read though because of its emphasis on loops and global mutable variables. I read though his description and this is how <I think> the Perceptron
You've probably built a neural network using PyTorch or Keras. You called model.fit(), watched the loss go down, and celebrated. But do you actually know what
Simulation-Based Verification of Neural Network Models EasyChair Preprint 15064 23 pages•Date: September 25, 2024 Abstract Neural networks (NNs) are increasingly being deployed in safety-critical applications such as autonomous vehicles, healthcare diagnostics, and robotics, where failures can have significant consequences. Verifying the behavior of these models is essential, yet traditional verification methods are often inadequate due to the complexity, non-linearity, and black-box nature of NNs
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Sequence processing with neural nets
Skip to content Zenke Lab Computational Neuroscience at the FMI Selected talks from the lab Research Funding Spiking Heidelberg Digits and Spiking Speech Commands Auryn Spiking Network Simulator LaTeX rebuttal/response to reviewers template Great free text books SuperSpike: Supervised learning in spiking neural networks — paper and code published May 28, 2018 fzenke I am happy to announce that the SuperSpike paper and code are finally published. Here is an example of a network with one hidden layer which
The world is a chaotic and confusing place. Could advanced artificial intelligence help us make sense of it? Well, possibly, except that today’s “artificial intelligences” are not exactly what you’d call sophisticated. With a couple of hundred virtual neurons (as opposed to 16 billion neurons in the human brain), the neural networks I work with can only do limited, narrow tasks. Can they digest a list of CNN headlines and predict plausible new headlines based on what they’ve seen? No, but it’s
NeurIPS Proceedings Search On the Inductive Bias of Neural Tangent Kernels Alberto Bietti, Julien Mairal Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Abstract State-of-the-art neural networks are heavily over-parameterized, making the optimization algorithm a crucial ingredient for learning predictive models with good generalization properties. A recent line of work has shown that in a certain over-parameterized regime, the learning dynamics of gradient descent are governed by a certa