This review covers original articles using deep learning in the biophotonic field published in
the last years. In these years deep learning, which is a subset of machine learning mostly …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

Deep neural networks (NNs) are powerful black box predictors that have recently achieved
impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in …

PowerPoint 演示文稿 Page 1 Uncertainty in Deep Learning Yarin Gal 2018.7.29 Page 2 Page
3 Different Uncertainties Two main types of uncertainty, often confused by practitioners, but …

We explore a recently proposed Variational Dropout technique that provided an elegant
Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case …

Deep learning tools have gained tremendous attention in applied machine learning.
However such tools for regression and classification do not capture model uncertainty. In …

We explore an as yet unexploited opportunity for drastically improving the efficiency of
stochastic gradient variational Bayes (SGVB) with global model parameters. Regular SGVB …

The most critical concern in machine learning is how to make an algorithm that performs well
both on training data and new data. No free lunch theorem implies that each specific task …

Dropout is a widely-used regularization technique, often required to obtain state-of-the-art
for a number of architectures. This work demonstrates that dropout introduces two distinct …