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Image classification on the MNIST and CIFAR-10 data sets

We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with high processing time on a single subject (2-3 hours). Moreover, even optimal surface registration does not necessarily produce optimal cortical parcellation as parcel boundaries are not fully matched to the geometric features. In this context, a choice of training features is important for

Abstract page for arXiv paper 2511.06696: Magnitude-Modulated Equivariant Adapter for Parameter-Efficient Fine-Tuning of Equivariant Graph Neural Networks

Implementing a paper in TensorFlow and annotating it

toomanyfeelings: iguanamouth: lewisandquark: I’ve been playing around with char-rnn, an open-source torch add-on for character-based neural networks by Andrej Karpathy, using it to generate everything from cookbook recipes to superhero names to a Lovecraft/cookbook mashup

# nnet: Feed-Forward Neural Networks and Multinomial Log-Linear Models Brian Ripley Software for feed-forward neural networks with a single hidden layer, and for multinomial log-linear models. Authors: nnet_7.3-21.zip (r-4.7-x86_64) nnet_7.3-21.zip (r-4.7-arm64) nnet_7.3-21.zip (r-4.6-x86_64) nnet_7.3-21.zip (r-4.6-arm64) nnet_7.3-21.tar.gz (r-4.7-arm64) nnet_7.3-21.tar.gz (r-4.7-x86_64) nnet_7.3-21.tar.gz (r-4.6-arm64) nnet_7.3-21.tar.gz (r-4.6-x86_64) nnet_7.3-21.tgz (r-4.6-emscripten) nnet/json (A

 Abstract—The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs

Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between

I just got back from ICLR 2019 and presented 2 posters, (and Michael gave a great talk!) at the Theoretical Physics Workshop on AI. To my amazement, some people still think that VC theory applies to Deep Learning, and that it is surprising that Deep Nets can overfit randomly labeled data. What's the story ? …