Add to ORKGWe introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework. We provide an efficient method to calculate the NTK of multilayer perceptrons. We compare the explicit differentiation implementation against autodifferentiation implementations, which have the benefit of extending the utility of the library to any architecture supported by PyTorch, such as convolutional networks. A feature of the library is that we expose the user to layerwise NTK components, and show that in some regimes a layerwise calculation is more memory efficient. We conduct preliminary experiments to demonstrate use cases for the software and probe the NTK.Comment: 19 pages, 5 figure
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Studying neural networks in the limit of infinite-width has provided us with numerous valuable theor...
Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent i...
Python library to train neural networks with a strong focus on hydrological applications. This pack...
In this book, I perform an experimental review on twelve similar types of Convolutional Neural Netwo...
Current state-of-the-art employs approximate multipliers to address the highly increased power deman...
Ultimate Anatome is a PyTorch library to analyze internal representation of neural networks. It is ...
In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously ap...
Empirical neural tangent kernels (eNTKs) can provide a good understanding of a given network's repre...
MemCNN is a PyTorch framework that simplifies the application of reversible functions by removing th...
Mean-field theory of neuronal networks has led to numerous advances in our analyticaland intuitive u...
We describe the notion of "equivalent kernels " and suggest that this provides a framework...
The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization...
Learning from structured data is a core machine learning task. Commonly, such data is represented as...
Building artificial neural networks and machine learning models in Python with PyTorch and TensorFlo
International audienceConvolutional Neural Networks (CNNs) [1] are the state of the art of image cla...
Studying neural networks in the limit of infinite-width has provided us with numerous valuable theor...
Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent i...
Python library to train neural networks with a strong focus on hydrological applications. This pack...
In this book, I perform an experimental review on twelve similar types of Convolutional Neural Netwo...