Adversarial examples—targeted, human-imperceptible modifications to a test input that cause a deep network to fail catastrophically—have taken the machine learning community by storm, with a large body of literature dedicated to understanding and preventing this phenomenon (see these ...
Adversarial examples—targeted, human-imperceptible modifications to a test input that cause a deep network to fail catastrophically—have taken the machine learning community by storm, with ...
While we primarily focus on image classification, we show that SWAG can significantly im- prove test perplexities of LSTM networks on language modeling problems, and in Appendix 7 we also ...
by Nicolas Papernot and Nicholas Frosst
Roles: data scientist Tools: spreadsheets, automated solutions (Weka, Trim, Trifacta Wrangler, RapidMiner), MLaaS (Google Cloud AI, Amazon Machine Learning, Azure Machine Learning) Data ...
H2O is a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. The dataset we are using is the Bank M…
Analytics engines can be powerful tools when you have a mountain of data and you know what valuable content you want to get out of it. But what happens when you don’t know what you’re ...
A guide to the ML Engineering Loop
Army researchers have developed a new approach for training machine learning models that can better withstand dirty and deceptive data. Models trained under this method have greatly ...
By combining adversarial training with a technique called randomized smoothing, which enlists math from the 19th century, Microsoft researchers created a method that establishes ...
Step-by-step Python machine learning tutorial for building a model from start to finish using Scikit-Learn. We'll have some fun and predict wine quality!
<p>Machine learning solutions, in particular those based on deep learning methods, form an underpinning for the modern artificial intelligence revolution that has dominated popular press ...