If a document is about travel, we may expect that short snippets of the document should also be about travel. We introduce a general framework for incorporating these types of invariances into a discriminative classifier. The framework imagines data as being drawn from a slice of a Lévy process. If we slice the Lévy process at an earlier point in time, we obtain additional pseudo-examples, which can be used to train the classifier. We show that this scheme has two desirable properties: it preserves the Bayes decision boundary, and it is equivalent to fitting a generative model in the limit where we rewind time back to 0. Our construction captures popular schemes such as Gaussian feature noising and dropout training, as well as admitting new generalizations. Black-box discriminative classifiers such as logistic regression, neural networks, and SVMs are the go-to solution in machine learning: they are simple to apply and often perform well. However, an expert may have additional knowledge to exploit, often taking the form of a certain family of transformations that should usually leave labels fixed. For example, in object recognition, an image of a cat rotated, translated, and peppered with a small amount of noise is probably still a cat.

Crowdsourcing enables researchers to conduct social experiments on a heterogenous set of participants and at a lower economic cost than conventional laboratory studies. For example, researchers can harness internet users to conduct user studies on their personal computers. Among various approaches to conduct subjective tests, pairwise comparisons are expected to yield more reliable results. However, in crowdsourced studies, the individuals performing the ratings are diverse compared to more controlled settings, which is difficult to control for using traditional experimental designs; researchers have recently proposed several randomized methods to conduct user studies [1, 2, 3], which accommodate incomplete and imbalanced data. HodgeRank, as an application of combinatorial Hodge theory to the preference or rank aggregation problem from pairwise comparison data, possibly being incomplete and imbalanced, was first introduced by [4], and inspired a series of studies in statistical ranking [5, 6, 7, 8]. Hodge theory has also found applications in game theory [9] and computer vision [10, 11], in addition to traditional applications in fluid mechanics [12] etc. HodgeRank formulates the ranking problem in terms of the discrete Hodge decomposition of the pairwise data and shows that it can be decomposed into three orthogonal components: a gradient flow representing a global rating (optimal in the L

Independent component analysis (ICA), as an approach to the blind source-separation (BSS) problem, has become the de-facto standard in many medical imaging settings. Despite successes and a large ongoing research effort, the limitation of ICA to square linear transformations have not been overcome, so that general INFOMAX is still far from being realized. As an alternative, we present feature analysis in medical imaging as a problem solved by Helmholtz machines, which include dimensionality reduction and reconstruction of the raw data under the same objective, and which recently have overcome major difficulties in inference and learning with deep and nonlinear configurations. We demonstrate one approach to training Helmholtz machines, variational auto-encoders (VAE), as a viable approach toward feature extraction with magnetic resonance imaging (MRI) data.

The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a 5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

Machine learning has taken a significant role in many data initiatives today. Facebook, for instance, is using machine learning to offer personalized ads, whilst Google uses it to learn about its users, and other technology companies are now able to crunch data in a fraction of the time. Organizations have been looking at machine learning as something that has the most use in looking at optimizing its current markets, but this may not be the case for too much longer. Several companies are now using machine learning combined with predictive analytics to help expand into new markets and exploit opportunities as soon as they come up. We heard about this from Wolf Rendall, Data Scientist at Auction.com, at last year's Social Media & Web Analytics Innovation Summit.

"Soon, our habitat will be invaded by unreal humans. Not only they'll influence our way of living, but also intervene in our modus operandi." I'm not the only one who thinks this way. Last week I released a list of must watch movies on Machine Learning and Data Science. I've watched 8 of them till now.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.

Developing robust quantitative trading strategies is an intensive, rigorous, time-consuming process with no guarantee for success. In this webinar, you will learn how to apply techniques from the Artificial Intelligence and machine learning fields to improve the quantitative strategy development process and maximize your chances of success with every strategy. Attendees will learn practical applications that they can apply to their own trading and will come away with a strategy they can actually trade live. Attendees should have a basic understanding of quantitative and algorithmic trading. No programming experience is required.

What an utterly interesting time to be alive. We all remember how Deep Blue defeated Kasparov. Chess became a'solved problem' pretty soon after that historic event. And in the past week, we have witnessed an even more amazing feat: AlphaGo beating world-class Go player Lee Sedol. The game that knows more positions than there are atoms in the universe was no match for Google's DeepMind team. It's fascinating to see how a game with relatively simple rules can lead to such complex and strategic gameplay.

Artificial intelligence isn't an industry so much as a technology poised to transform business across a wide variety of sectors--and probably more than you think. During a panel discussion Tuesday at the Las Vegas tech trade show CES, a group of A.I. experts talked about which industries are the most ripe for adoption and application of A.I., and why entrepreneurs and consumers alike stand to benefit significantly from the technology. The number one industry set be transformed by A.I. appears to be healthcare, with 400 million invested by health care companies in the technology as of last year, a figure that's projected to grow to 3 billion or more by 2020, according to data from the Beacon Center for the Study of Evolution in Action. The retail industry is close behind, with 100 million invested as of 2015, expected to reach 1.9 billion by 2020. Panelists pointed to manufacturing, financial services and government as the three followers to healthcare and retail.