The construction of confidence intervals for the mean of a bounded random variable is a classical problem in statistics with numerous applications in machine learning and virtually all scientific fields. In particular, obtaining the tightest possible...
Knowledge graphs contain rich relational structures of the world, and thus complement data-driven machine learning in heterogeneous data. One of the most effective methods in representing knowledge graphs is to embed symbolic relations and entities i...
The recent advances in machine learning algorithms have boosted the application of these techniques to the field of condensed matter physics, in order e.g. to classify the phases of matter at equilibrium or to predict the real-time dynamics of a larg...
Spear Phishing is a harmful cyber-attack facing business and individuals worldwide. Considerable research has been conducted recently into the use of Machine Learning (ML) techniques to detect spear-phishing emails. ML-based solutions may suffer from...
Data cleansing is a typical approach used to improve the accuracy of machine learning models, which, however, requires extensive domain knowledge to identify the influential instances that affect the models. In this paper, we propose an algorithm tha...
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In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will. I focus relatively narrowly on a notion that I call "Knightian freedom": a cer...
The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in con...
Turing's famous `machine' model constitutes the first intuitively convincing framework for computing with real numbers. Kleene's computation schemes S1-S9 extend Turing's approach to computing with objects of any finite type. Both frameworks have the...
We describe the Turing Machine, list some of its many influences on the theory of computation and complexity of computations, and illustrate its importance....
com/wapost.jpg Archived 2011-10-09 at the Wayback Machine Johnson, Ken (1 November 2002). "Taylor Davis". The New York Times. Retrieved 2017-08-14. "Taylor Davis
As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produc...
Artificial intelligence courses can help you learn machine learning algorithms, neural networks, natural language processing, and computer vision techniques. Compare course options to find what fits your …
Beginner artificial intelligence (AI) courses can help you learn core concepts such as machine learning basics, model behavior, and simple classification or prediction tasks. Compare course options to find …
Artificial intelligence courses can help you learn machine learning algorithms, natural language processing, computer vision, and neural networks. Compare course options to find what fits your …
The Capital One automatic teller machine (ATM) in Broussard, LA, offers convenient banking access for customers in the area. Located in a safe and well-lit spot, this ATM allows users to …
Machine learning (ML) recourse techniques are increasingly used in high-stakes domains, providing end users with actions to alter ML predictions, but they assume ML developers understand what input variables can be changed. However, a recourse plan's...
Scientific machine learning (SciML) methods such as physics-informed neural networks (PINNs) are used to estimate parameters of interest from governing equations and small quantities of data. However, there has been little work in assessing how well...