Abstract--Disagreement between two classifiers regarding the class membership of an observation in pattern recognition can be indicative of an anomaly and its nuance. As in general classifiers base their decision on class aposteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergence. However, existing divergences are not particularly suitable to gauge classifier incongruence. In this paper, we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure, referred to as Delta divergence. In contrast to existing measures, it is decision cognizant. The focus in Delta divergence on the dominant hypotheses has a clutter reducing property, the significance of which grows with increasing number of classes. The proposed measure satisfies other important properties such as symmetry, and independence of classifier confidence. The relationship of the proposed divergence to some baseline measures is demonstrated experimentally, showing its superiority. Divergence in information theory has been intensively studied and researched over the last six decades. On one hand the massive interest in the subject has been driven by the diversity of applications where divergence plays the key role as an objective function. On the other hand the investigation of the underlying theoretical properties of divergence has motivated the discovery of new measures with tailor made characteristics that are fine tuned for specific applications.

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as ɛ-greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.

This technical note extends recent results on the computational complexity of globally minimizing the error of piecewise-affine models to the related problem of minimizing the error of switching linear regression models. In particular, we show that, on the one hand the problem is NP-hard, but on the other hand, it admits a polynomial-time algorithm with respect to the number of data points for any fixed data dimension and number of modes.

Intro for this event: Come see examples of how today's large data collections are being tackled by data science and machine learning methods, thereby empowering a data-driven transformation in organizations and industries that is bringing about greater competitive intelligence, insights, and innovation. Speaker Bio: Dr. Kirk Borne is the Principal Data Scientist for NextGen Analytics and Data Science in the Strategic Innovation Group at Booz Allen Hamilton. He previously spent 12 years as Professor at George Mason University in the Computational and Data Sciences program. Before that, he worked 18 years on various NASA contracts, as research scientist and as manager on large data systems. He has a PhD in Astrophysics from Caltech.

During his 15 years as president of the Service Employees International Union, Andy Stern was a controversial figure. He suffered his share of criticism from inside and outside the union. There was, however, no disputing his success in making SEIU the largest and fastest growing union in the country and a powerful political machine that was instrumental in electing President Obama and getting the Affordable Care Act passed. During Stern's tenure as national organizing director and president, he introduced and implemented strategies of industry-wide organizing and bargaining to counter the changing reality of employers who were becoming large and international. He took SEIU out of the AFL-CIO and formed a new labor federation called Change to Win, because he felt the mainstream labor movement was too conservative about organizing and limited its power by refusing to consolidate smaller unions into bigger and more powerful ones.

Ashok Goel wants to expand high-quality education to "millions" more people over the internet. It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities -- and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider.

In a 1942 short story called Runaround, science fiction author Isaac Asimove formulated his famous'Three Laws of Robotics'. As per the Handbook of Robotics, 56th Edition, 2058 AD, the three laws are: A robot may not injure a human being or, through inaction, allow a human being to come to harm; robot must obey the orders given it by human beings except where such orders would conflict with the First Law; and a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws. While Asimove created these laws as a literary device - both to provide an ethical framework for sentient machines that were smarter than humans, and to find drama in situations where, inevitably, the laws came across a loophole, or became self-contradictory - today's world needs ethical guidelines for machine intelligence that can soon become so smart that they leave humans far behind. In a piece at Slate.com, Nadella, lays down his own laws for AI. AI must be designed to assist humanity.

Miniaturization is one of the most world-shaking trends of the last several decades. Computer chips now have features measured in billionths of a meter. Sensors that once weighed kilograms fit inside your smartphone. Researchers are aiming to take sensors smaller--much smaller. In a new University of Stuttgart paper published in Nature Photonics, scientists describe tiny 3D printed lenses and show how they can take super sharp images.

If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R?a cross-platform, zero-cost statistical programming environment?there The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail.

The Human Vaccines Project is teaming with the University of California San Diego to apply advances in machine learning to solve critical problems impeding the development of vaccines and therapeutics for a wide range of diseases. The Human Vaccines Project (Project) is a new global public-private partnership of academic research centers, industry, non-profits and government agencies designed to accelerate the development of next-generation vaccines and immunotherapies. On Friday, July 8, the California Institute for Telecommunications and Information Technology (Calit2) Qualcomm Institute (QI) will host an invitation-only Workshop on Human Vaccines and Machine Learning (HVML) in Atkinson Hall on the UC San Diego campus. The workshop will bring together top academic researchers and partners in the vaccine development community from the biotech and pharmaceutical industries, as well as experts from top software companies and IT research organizations. "The Human Vaccines Project has embarked on a decade-long, 1 billion mission to decode the human immune system," said Wayne C. Koff, Ph.D., President and CEO of the Human Vaccines Project.