In this paper, we study the challenge of feature selection based on a relatively small collection of sample pairs $\{(x_i, y_i)\}_{1 \leq i \leq m}$. The observations $y_i \in \mathbb{R}$ are thereby supposed to follow a noisy single-index model, depending on a certain set of signal variables. A major difficulty is that these variables usually cannot be observed directly, but rather arise as hidden factors in the actual data vectors $x_i \in \mathbb{R}^d$ (feature variables). We will prove that a successful variable selection is still possible in this setup, even when the applied estimator does not have any knowledge of the underlying model parameters and only takes the 'raw' samples $\{(x_i, y_i)\}_{1 \leq i \leq m}$ as input. The model assumptions of our results will be fairly general, allowing for non-linear observations, arbitrary convex signal structures as well as strictly convex loss functions. This is particularly appealing for practical purposes, since in many applications, already standard methods, e.g., the Lasso or logistic regression, yield surprisingly good outcomes. Apart from a general discussion of the practical scope of our theoretical findings, we will also derive a rigorous guarantee for a specific real-world problem, namely sparse feature extraction from (proteomics-based) mass spectrometry data.

An exciting new study from the University of Sheffield and published in the journal Swarm Intelligence has demonstrated (free pre-print version) a method of allowing computers to make sense of complex patterns all on their own, an ability that could open the door to some of the most advanced and speculative applications of artificial intelligence. Using an all-new technique called Turing Learning, the team managed to get an artificial intelligence to watch movements within a swarm of simple robots and figure out the rules that govern their behavior. It was not told to look for any particular signifier of swarm behavior, but simply to try to emulate the source more and more accurately and to learn from the results of that process. It's a simple system that the researchers think could be applied everywhere from human and animal behavior to biochemical analysis to personal security. Alan Turing was a multi-talented British mathematician who helped to both win the Second World War and invent the earliest computers, both while leading the Allied code-breaking efforts at Blechley Park.

Electric utilities across the U.S. are wasting no time to take advantage of FAA rules authorizing use of drones for commercial purposes. Electric utilities across the U.S. are wasting no time taking advantage of new FAA rules authorizing use of drones for commercial purposes. "We've certainly heard from our members that they're excited about this technology," said Chris Hickling, the director of government relations for the Edison Electric Institute (EEI), the trade group for investor-owned utilities in the U.S. "They see it as part of building a smarter infrastructure. We see it as an area that's going to continue to grow." More than 20 utilities have already tested unmanned aerial vehicles for inspecting transmission and distribution lines for damage from storm and normal wear and tear, using temporary rules from the Federal Aviation Administration, and are now ready to demonstrate them even more.

I'm working my way through the Coursera course on Machine learning by Andrew Ng, and I'm really confused about the bias unit? In the programming assignment, we always set the parameter for the bias unit to 0, so how does it affect the neural network in any way? Since the bias unit will be multiplied by it's parameter/weight (which seems to always be zero), wouldn't it be completely insignificant?

Salesforce has spent over 4 billion on acquisitions in the past year alone, and it's making some investors grow concerned about the company's spending strategy. According to a note by Macquarie Research on Tuesday, Salesforce may have some explaining to do during its earnings call on Wednesday to ease the investors worried about the company's record-high buying spree over the past year. That includes the 2.8 billion Demandware acquisition, which was the largest deal Salesforce has made to date, and the 750 million deal for the 40-person startup Quip. Still, Piper Jaffray noted that most of the acquisitions make sense because they've been in the artificial intelligence and machine learning space, in which Salesforce is launching its new product Einstein.

It is already changing the face of warface, with AI adversaries and electronic spies taking centre stage. However, America has fallen catastrophically behind in the hi-tech battlefield, a new report has claimed. The Defense Science Board's report into autonomy concluded'there are both substantial operational benefits and potential perils associated with its use,' and called for immediate action. The Defense Science Board's report into autonomy concluded the DoD must accelerate its exploitation of autonomy to remain ahead of enemies. 'This study concluded that DoD must accelerate its exploitation of autonomy--both to realize the potential military value and to remain ahead of adversaries who also will exploit its operational benefits.'

Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis. You can read more about Prof. Rigollet's work and courses on his website.

A benefit of using ensembles of decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from a trained predictive model. In this post you will discover how you can estimate the importance of features for a predictive modeling problem using the XGBoost library in Python. Feature Importance and Feature Selection With XGBoost in Python Photo by Keith Roper, some rights reserved. XGBoost is the high performance implementation of gradient boosting that you can now access directly in Python. A benefit of using gradient boosting is that after the boosted trees are constructed, it is relatively straightforward to retrieve importance scores for each attribute.

A research team from Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has developed an artificial intelligence (AI) method, aimed at training computers to interpret pathology images. The team trained the computer to distinguish between cancerous tumor regions and normal regions based on a deep multi-layer convolutional network. In an objective evaluation in which researchers were given slides of lymph node cells and asked to determine whether or not they contained cancer, the team's automated diagnostic method proved accurate approximately 92 per cent of the time. One of the researchers, Aditya Khosla, said, "This nearly matched the success rate of a human pathologist, whose results were 96 percent accurate." "In our approach, we started with hundreds of training slides for which a pathologist has labeled regions of cancer and regions of normal cells," said Dayong Wang.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display