Similarity learning has received a large amount of interest and is an important tool for many scientific and industrial applications. In this framework, we wish to infer the distance (similarity) between points with respect to an arbitrary distance function $d$. Here, we formulate the problem as a regression from a feature space $\mathcal{X}$ to an arbitrary vector space $\mathcal{Y}$, where the Euclidean distance is proportional to $d$. We then give Rademacher complexity bounds on the generalization error. We find that with high probability, the complexity is bounded by the maximum of the radius of $\mathcal{X}$ and the radius of $\mathcal{Y}$.

This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, 1) the contextual information of a scene along with low-level visual cues modulates attention, 2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensemble's members. Keywords: Visual attention, saliency prediction, fixation prediction, inter-image similarity, extreme learning machines 1. Introduction The fixation prediction, also known as saliency modeling, is associated with the estimation of a saliency map, the probability map of the locations an observer will be looking at for a long enough period of time meanwhile viewing a scene. It is part of the computational perspective of visual attention [1], the process of narrowing down the available visual information upon which to focus for enhanced processing. Corresponding author Email address: hamed.r-tavakoli@aalto.fi

Chinese technology company LeEco on has made an inauspicious entry into the race to develop self-driving electric cars after its prototype car could not make it down the runway at a San Francisco launch event. China's Le Holdings Co Ltd, also known as LeEco, planned to unveil its self-driving car prototype as part of a splashy U.S. launch for an array of technology products and services, including phones, televisions and entertainment production. However, the planned climax to its two-hour press conference had to be scrapped at the last minute after one of its prototype electric cars was involved in a crash during its drive from Los Angeles to San Francisco. A second was flown over from London, where it is appearing in a forthcoming Transformers movie directed by Michael Bay, but the flight was delayed, missing the beginning of the show. The firm had planned to unveil its self-driving car prototype as part of a splashy U.S. launch for an array of technology products and services in San Francisco.

Machine-learning provides a theoretical framework for the discovery and prioritization of bioactive compounds with desired pharmacological effects and their optimization as drug-like leads. Biological target identification and protein design are emerging areas of application. Among the many machine-learning approaches in molecular informatics, chemocentric methods have found widespread application. Their underlying logic typically follows three steps. First, there is the selection of a problem-specific set of descriptors that are believed to capture the essential properties of the molecules involved.

Despite the best efforts of movies like Ex Machina, Morgan and Avengers: Age of Ultron, a new survey found that people from around the world largely see artificial intelligence having a more positive than negative impact on their lives and society in general. Communications firm Weber Shandwick has just published its "AI-Ready or Not: Artificial Intelligence Here We Come!" report, conducted with KRC Research, for marketers, surveying 2,100 consumers across five global markets on AI, its many uses, how they see it evolving, and how comfortable they are with that development. But don't go tearing up your plans for an unconnected cabin in the woods just yet, because even though consumer survey respondents were seven times more likely to see the sunny side of AI, a full one-third of respondents also admitted to knowing nothing about AI at all. The survey also interviewed 150 marketing executives (primarily CMOs) in the U.S., the U.K., and China responsible for the oversight and execution of marketing or branding activities at their organizations. On the consumer side, 77% of respondents would like AI's development to accelerate or remain at its current pace, two-thirds or more trust AI with handling medication reminders, travel directions, entertainment, targeted news, and manual labor and mechanics.

Hidden Decision Trees is a statistical and data mining methodology (just like logistic regression, SVM, neural networks or decision trees) to handle problems with large amounts of data, non-linearities and strongly correlated dependent variables. The technique is easy to implement in any programming language. It is more robust than decision trees or logistic regression, and help detect natural final nodes. Implementations typically rely heavily on large, granular hash tables. No decision tree is actually built (thus the name hidden decision trees), but the final output of an hidden decision tree procedure consists of a few hundred nodes from multiple non-overlapping small decision trees.

PyData Chicago 2016 This tutorial provides you with a comprehensive introduction to machine learning in Python using the popular scikit-learn library. We will learn how to tackle common problems in predictive modeling and clustering analysis that can be used in real-world problems, in business and in research applications. And we will implement certain algorithms as scratch as well, to internalize the inner workings This tutorial will teach you the basics of scikit-learn. We will learn how to leverage powerful algorithms from the two main domains of machine learning: supervised and unsupervised learning. In this talk, I will give you a brief overview of the basic concepts of classification and regression analysis, how to build powerful predictive models from labeled data.

Quantum physics has some spooky, anti-intuitive effects, but it could also be essential to how actual intuition works, at least in regards to artificial intelligence. In a new study, researcher Vedran Dunjko and co-authors applied a quantum analysis to a field within artificial intelligence called reinforcement learning, which deals with how to program a machine to make appropriate choices to maximize a cumulative reward. The field is surprisingly complex and must take into account everything from game theory to information theory. Dunjko and his team found that quantum effects, when applied to reinforcement learning in artificial intelligence systems, could provide quadratic improvements in learning efficiency, reports Phys.org . Exponential improvements might even be possible over short-term performance tasks.

It does not pay to underestimate a monkey with a rock. Scientists studying the stone-smashing habits of bearded capuchin monkeys in Brazil have found that the primates inadvertently produce stone flakes that look very similar to the flakes used as cutting tools by early humans. The findings, published in the journal Nature, could snarl the links that paleoanthropologists make between early Stone Age artifacts and the emergence of primitive human technology. "It does raise interesting questions about the level of cognitive complexity -- how intelligent a hominin has to be in order to produce what we thought was a sophisticated technology," said lead author Tomos Proffitt, a paleoanthropologist at Oxford University. When anthropologists explore early human settlements, they typically search for signs of tool use, whether by looking at the cuts on butchered animal bones or finding the tools themselves.

They're practically everywhere, and it's no wonder: according to some analysts, the fitness tracker market is one poised to reach 19 billion in 2018. But some companies are positioned better than others. On Wednesday, one of the arguable forerunners, Moov, took the wraps off the Moov HR, an activity tracker that measures heart rate with pinpoint accuracy. The Moov HR builds on the foundation of the startup's previous sensor, the Moov Now, which garnered praise for its innovative approach to fitness. Rather than simply measuring the number of steps you've taken, the calories you've burned, and distance you've walked, it fed that data into an artificial intelligence that dynamically guided you through goal-oriented workouts.