Computed tomography (CT) generates a stack of cross-sectional images covering a region of the body. The visual assessment of these images for the identification of potential abnormalities is a challenging and time consuming task due to the large amount of information that needs to be processed. In this article we propose a deep artificial neural network architecture, ReCTnet, for the fully-automated detection of pulmonary nodules in CT scans. The architecture learns to distinguish nodules and normal structures at the pixel level and generates three-dimensional probability maps highlighting areas that are likely to harbour the objects of interest. Convolutional and recurrent layers are combined to learn expressive image representations exploiting the spatial dependencies across axial slices. We demonstrate that leveraging intra-slice dependencies substantially increases the sensitivity to detect pulmonary nodules without inflating the false positive rate. On the publicly available LIDC/IDRI dataset consisting of 1,018 annotated CT scans, ReCTnet reaches a detection sensitivity of 90.5% with an average of 4.5 false positives per scan. Comparisons with a competing multi-channel convolutional neural network for multi-slice segmentation and other published methodologies using the same dataset provide evidence that ReCTnet offers significant performance gains.

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for 'tricking' the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product - the classifiers - that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.

Thanks to the rise of wearable and connected devices, sensor-generated time series comprise a large and growing fraction of the world's data. Unfortunately, extracting value from this data can be challenging, since sensors report low-level signals (e.g., acceleration), not the high-level events that are typically of interest (e.g., gestures). We introduce a technique to bridge this gap by automatically extracting examples of real-world events in low-level data, given only a rough estimate of when these events have taken place. By identifying sets of features that repeat in the same temporal arrangement, we isolate examples of such diverse events as human actions, power consumption patterns, and spoken words with up to 96% precision and recall. Our method is fast enough to run in real time and assumes only minimal knowledge of which variables are relevant or the lengths of events. Our evaluation uses numerous publicly available datasets and over 1 million samples of manually labeled sensor data.

Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling reduces the generalization error. Finally I show different methods of ensembling, together with their results and code to try it out for yourself. This is how you win ML competitions: you take other peoples' work and ensemble them together." The most basic and convenient way to ensemble is to ensemble Kaggle submission CSV files. You only need the predictions on the test set for these methods -- no need to retrain a model. This makes it a quick way to ensemble already existing model predictions, ideal when teaming up. Let's see why model ensembling reduces error rate and why it works better to ensemble low-correlated model ...

Machine learning (ML) is such a hot area in security right now. At the 2016 RSA Conference, you would be hard pressed to find a company that is not claiming to use ML for security. To the layperson, ML seems like the magic solution to all security problems. Take a bunch of unlabeled data, pump it through a system with some ML magic inside, and it can somehow identify patterns even human experts can't find -- all while learning and adapting to new behaviors and threats. Rather than having to code the rules, these systems can discover the rules all by themselves.

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V -statistics, which are degenerate under the null hypothesis, and non-degenerate elsewhere. To illustrate this approach, we construct a two-sample test, an instantaneous independence test and a multiple lag independence test for time series. In experiments, the wild bootstrap gives strong performance on synthetic examples, on audio data, and in performance benchmarking for the Gibbs sampler.

You may have seen our previous posts on machine learning -- specifically, how to let your code learn from text and working with stop words, stemming, and spam. So today, we're going to build our machine learning-based spam filter, using the tools we walked through in those posts: tokenizer, stemmer, and naive bayes classifier. We are going to work with bluebird promise library here, so if you are not used to promises, please take a look at the bluebird API reference. Before we begin, it's important to have good training data. You can download some here -- we are interested in two.

Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .

Can Google bring peace to the web with machine learning? Jigsaw, a subsidiary of parent company Alphabet is certainly trying, building open-source AI tools designed to filter out abusive language. A new feature from Wired describes how the software has been trained on some 17 million comments left underneath New York Times stories, along with 13,000 discussions on Wikipedia pages. This data is labeled and then fed into the software -- called Conversation AI -- which begins to learn what bad comments look like. According to the report, Google says Conversation AI can identify abuse with "more than 92 percent certainty and a 10 percent false-positive rate" when compared to the judgements of a human panel.

A subsidiary of Google's parent company Alphabet, Jigsaw, is using machine learning to fend off online trolling, reports Wired. The New York–based think tank is building open-source AI tools, collectively called Conversation AI, to filter out harassment and abusive language. "Few things poison conversations online more than abusive language, threats, and harassment," reads the Conversation AI website. "We're studying how computers can learn to understand the nuances and context of abusive language at scale. If successful, machine learning could help publishers and moderators improve comments on their platforms and enhance the exchange of ideas on the internet."