A significantly faster algorithm is presented for the original kNN mode seeking procedure. It has the advantages over the well-known mean shift algorithm that it is feasible in high-dimensional vector spaces and results in uniquely, well defined modes. Moreover, without any additional computational effort it may yield a multi-scale hierarchy of clusterings. The time complexity is just O(n^1.5). resulting computing times range from seconds for 10^4 objects to minutes for 10^5 objects and to less than an hour for 10^6 objects. The space complexity is just O(n). The procedure is well suited for finding large sets of small clusters and is thereby a candidate to analyze thousands of clusters in millions of objects. The kNN mode seeking procedure can be used for active learning by assigning the clusters to the class of the modal objects of the clusters. Its feasibility is shown by some examples with up to 1.5 million handwritten digits. The obtained classification results based on the clusterings are compared with those obtained by the nearest neighbor rule and the support vector classifier based on the same labeled objects for training. It can be concluded that using the clustering structure for classification can be significantly better than using the trained classifiers. A drawback of using the clustering for classification, however, is that no classifier is obtained that may be used for out-of-sample objects.

The design of neural network architectures for a new data set is a laborious task which requires human deep learning expertise. In order to make deep learning available for a broader audience, automated methods for finding a neural network architecture are vital. Recently proposed methods can already achieve human expert level performances. However, these methods have run times of months or even years of GPU computing time, ignoring hardware constraints as faced by many researchers and companies. We propose the use of Monte Carlo planning in combination with two different UCT (upper confidence bound applied to trees) derivations to search for network architectures. We adapt the UCT algorithm to the needs of network architecture search by proposing two ways of sharing information between different branches of the search tree. In an empirical study we are able to demonstrate that this method is able to find competitive networks for MNIST, SVHN and CIFAR-10 in just a single GPU day. Extending the search time to five GPU days, we are able to outperform human architectures and our competitors which consider the same types of layers.

Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions. The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions. Since in the general case this is not possible, in order to speed up the convergence of the chain, it is required to generate auxiliary samples whose information is eventually disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. This novel scheme arises naturally after pointing out the relationship between the standard Gibbs sampler and the chain rule used for sampling purposes. Numerical simulations involving simple and real inference problems confirm the excellent performance of the proposed scheme in terms of accuracy and computational efficiency. In particular we give empirical evidence of performance in a toy example, inference of Gaussian processes hyperparameters, and learning dependence graphs through regression.

Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multi-path, and non-line-of-sight (NLOS) propagation. Furthermore, building the fingerprints is a very time consuming process. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, the prediction probability of our proposed approach is improved significantly. The process for building fingerprints can also be reduced drastically. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral (USRP) platform with four antennas.

Time series interpretation aims to provide an explanation of what is observed in terms of its underlying processes. The present work is based on the assumption that common classification-based approaches to time series interpretation suffer from a set of inherent weaknesses whose ultimate cause lies in the monotonic nature of the deductive reasoning paradigm. In this document we propose a new approach to this problem based on the initial hypothesis that abductive reasoning properly accounts for the human ability to identify and characterize patterns appearing in a time series. The result of the interpretation is a set of conjectures in the form of observations, organized into an abstraction hierarchy, and explaining what has been observed. A knowledge-based framework and a set of algorithms for the interpretation task are provided, implementing a hypothesize-and-test cycle guided by an attentional mechanism. As a representative application domain, the interpretation of the electrocardiogram allows us to highlight the strengths of the proposed approach in comparison with traditional classification-based approaches.

Well, this year's videos are getting creative! Have a holiday robot video of your own that you'd like to share? Send your submissions to editors@robohub.org. "Don't be late for Christmas!" by FZI Living Lab Warning: This video is insane . . .

DigitalGenius, a fledgling artificial intelligence (AI) startup that's setting out to automate many facets of customer service, has announced a $14.75 million series A funding round led by Global Founders Capital, with participation from Salesforce Ventures, MMC Ventures, Paua Ventures, Kairos, Runa Capital, RRE Ventures, Lumia Capital, Compound, Spider Capital, and Lerer Hippeau Ventures. Founded out of London in 2013, DigitalGenius connects with companies' CRM and customer service platforms to train AI assistants using transcripts from historical customer service interactions. Using these learnings, the AI is able to predict relevant meta-data about a new case in real time, and even channel a query to the most relevant (human) team members based on the content. It's more about helping human customer service personnel respond quickly to queries using historical precedence, so that when a message is received over email, social media, or other text-based messaging platforms, DigitalGenius can suggest the best answer for an agent to approve. In theory, the model should improve with each human interaction.

This is a key question at the heart of efforts to tackle money laundering: if you work for a bank or other financial institution and have suspicions money laundering is happening, you have a legal duty to speak up. Your suspicion could be based on pure intuition – a sense that something just doesn't quite add up – but the law nonetheless expects you to act. Across a huge institution, however, processing millions of transactions per hour, only a tiny share of customers will meet an actual human being. Could software be taught the human intuition that senses something is not quite right? Or can it pick out suspicious behaviour that even humans might not notice?

They're often depicted as being lazy, but a new study suggests that teenagers are going through one of the best times to learn a new skill. Scientists have discovered increased activity in an area of the brain called the striatum in 17-20 year-olds, which boosts the way they learn from feedback. The findings suggest that adolescence is a unique life phase for increased feedback-learning performance. The researchers studied over 230 participants aged eight to 25. Each participant completed a feedback learning task, in which good performance was rewarded with positive feedback.

France's data privacy watchdog may fine WhatsApp if it does not comply with an order to bring its sharing of user data with parent company Facebook into line with French privacy law. CNIL, the French data protection authority, has told WhatsApp to comply with the order within one month, and pay particular attention to obtaining users' consent. If WhatsApp doesn't comply, it could sanction the company, CNIL said. France's data privacy watchdog may fine WhatsApp if it does not comply with an order to bring its sharing of user data with parent company Facebook into line with French privacy law (stock image) WhatsApp said it would begin sharing some user data with the Facebook in 2016, drawing warnings from European privacy watchdogs about getting the appropriate consent. In October, European Union privacy regulators criticised WhatsApp for not resolving their concerns over the messaging service's sharing of user data with Facebook a year after they first issued a warning.