Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

Abstract: this research explores the vulnerability of convolutional neural networks (CNNs) to adversarial attacks, with a focus on the Fast Gradient Sign Method (FGSM) as a baseline threat model. The ...

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...

"The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The ...

Deep learning, which is basically neural network machine learning with multiple hidden layers, is all the rage—both for problems that justify the complexity and high computational cost of deep ...

"In the [MNIST tutorial](https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/MNIST.ipynb) we use an lmdb database. You can also use leveldb or even ...