I’ve been doing some flying, which gives me the opportunity to see various movies on that little seat-back screen. And some of these movies have been pretty good:

NL2Bash: A Corpus and Semantic Parser for Natural Language Interface to the Linux Operating System

To perform a visual search instead of asking for something by voice or text, you show what you are looking for. As the old saying goes, “A picture is worth a thousand words.” Often it’s easier to show a physical example or image than to try to describe an item with words that a search engine can effectively use. Some users might simply lack the knowledge or understanding to describe a specialized item. Another use case for visual search is when image or video data must be searchable, but it’s ingested too fast to accurately assign labels or metadata.

Design and Analysis of Locally or Globally Efficient Adaptive Designs (adpss)Provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exp …

This post is authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at Microsoft.

Metrics for Multiple Testing with Correlated Outcomes (NRejections)Implements methods in Mathur and VanderWeele (in preparation) to characterize global evidence strength across W correlated ordinary least squares (OLS) …

There are many fields under the umbrella of the data science and sometimes these roles look similar to each other or are used interchangeably. Let us list these terms first and try to understand them

Today’s R tip is: put your values in columns.

If you’re familiar with analyzing data in Excel and want to learn how to work with the same data in R, Alyssa Columbus has put together a very useful guide: How To Use R With Excel. In addition to providing you with a guide for installing and setting up R and the RStudio IDE, it provide a wealth of useful tips for working with Excel data in R, including:

We prove that idealised discriminative Bayesian neural networks, capturing perfect epistemic uncertainty, cannot have adversarial examples: Techniques for crafting adversarial examples will necessarily fail to generate perturbed images which fool the classifier. This suggests why MC dropout-based techniques have been observed to be fairly robust to adversarial examples. We support our claims mathematically and empirically. We experiment with HMC on synthetic data derived from MNIST for which we know the ground truth image density, showing that near-perfect epistemic uncertainty correlates to density under image manifold, and that adversarial images lie off the manifold. Using our new-found insights we suggest a new attack for MC dropout-based models by looking for imperfections in uncertainty estimation, and also suggest a mitigation. Lastly, we demonstrate our mitigation on a cats-vs-dogs image classification task with a VGG13 variant. Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study