Sparse Bayesian learning is one of the state-of- the-art machine learning algorithms, which is able to make stable and reliable probabilistic predictions. However, some of these algorithms, e.g. probabilistic classification vector machine (PCVM) and relevant vector machine (RVM), are not capable of eliminating irrelevant and redundant features which could lead to performance degradation. To tackle this problem, in this paper, we propose a sparse Bayesian classifier which simultaneously selects the relevant samples and features. We name this classifier a probabilistic feature selection and classification vector machine (PFCVM), in which truncated Gaussian distributions are em- ployed as both sample and feature priors. In order to derive the analytical solution for the proposed algorithm, we use Laplace approximation to calculate approximate posteriors and marginal likelihoods. Finally, we obtain the optimized parameters and hyperparameters by the type-II maximum likelihood method. The experiments on synthetic data set, benchmark data sets and high dimensional data sets validate the performance of PFCVM under two criteria: accuracy of classification and efficacy of selected features. Finally, we analyze the generalization performance of PFCVM and derive a generalization error bound for PFCVM. Then by tightening the bound, we demonstrate the significance of the sparseness for the model.

We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the posterior probability for every possible model explaining a two body system using Jeffreys prior. The approach does not rely on the optimization of any cost functions and consequently is much faster than existing algorithms. Despite this time and computational advantage, numerical results show that for several sparse topologies the algorithm is comparable to the best existing algorithms, and is more accurate in the presence of hidden variables. We apply this approach to the analysis of US stock market data and to neural data, in order to show its efficiency in recovering robust statistical dependencies in real data with non-stationary correlations in time and/or space.

ABSTRACT A new type of End-to-End system for text-dependent speaker verification is presented in this paper. Previously, using the phonetic/speaker discriminative DNNs as feature extractors for speaker verification has shown promising results. The extracted frame-level (DNN bottleneck, posterior or d-vector) features are equally weighted and aggregated to compute an utterance-level speaker representation (d-vector or i-vector). In this work we use speaker discriminative CNNs to extract the noise-robust frame-level features. These features are then combined to form an utterance-level speaker vector through an attention mechanism. The proposed attention model takes the speaker discriminative information and the phonetic information to learn the weights. The whole system, including the CNN and attention model, is joint optimized using an end-to- end criterion. The algorithm can automatically select the most similar impostor for each target speaker to train the network. We demonstrated the effectiveness of the proposed end-to-end system on Windows 10 "Hey Cortana" speaker verification task. Index Terms-- speaker verification, end-to-end training, attention model, deep learning, CNN 1. INTRODUCTION Speaker verification (SV) is a binary classification problem in which a person's identity is verified based on his/her voice.

From Amazon and Facebook to Google and Microsoft, leaders of the world's most influential technology firms are highlighting their enthusiasm for Artificial Intelligence (AI). While there is growing interest in AI, the field is understood mainly by specialists. Our goal for this primer is to make this important field accessible to a broader audience. We'll begin by explaining the meaning of'AI' and key terms including'machine learning'. We'll illustrate how one of the most productive areas of AI, called'deep learning', works.

Dan Bricklin, of Newton, Mass., inventor of the personal computer spreadsheet, stands with a laptop computer in his Newton home, Wednesday, May 24, 2006 (AP Photo/Steven Senne) When President Carter talked to Americans in July 1979 about their crisis of confidence--"the erosion of our confidence in the future is threatening to destroy the social and the political fabric of America"--Dan Bricklin and Bob Frankston were witnessing the rapidly rising confidence in their electronic spreadsheet, the first killer app for the PC. A little over three years later, Time magazine named the PC "the Machine of the Year," observing that this innovation "happened to pop up when it did, right now, at this point in time, like the politicians call it, because we were getting hungry to be ourselves again." This week's milestones in the history of technology include the birth of VisiCalc, the world's first handheld-sized scientific calculator, and Daguerreotype photography. Software Arts is incorporated by founders Dan Bricklin and Bob Frankston for the purpose of developing VisiCalc, the world's first spreadsheet program, which will be published by a separate company, Personal Software Inc. (later named VisiCorp). VisiCalc will come to be widely regarded as the first "killer app" that turned the PC into a serious business tool.

This tutorial introduces machine learning and data science concepts for developers. On the web, we already have many excellent resources for learning data science, however, the sheer amount of material can, in itself, be daunting. This is based on my insights from the Enterprise AI course and also the Data Science for IoT course which I teach at Oxford University. I also address a broader question: Which maths and stats techniques do you need for data science? A knowledge of algorithms (maths and stats) is the main differentiator between traditional programming and analytics-based programming. Having said that, it helps to start with programming and approach the maths (initially) through APIs and libraries.

Welcome to the Fourth Industrial Revolution, or what the World Economic Forum calls the "fusion of technologies that is blurring the lines between the physical, digital, and biological spheres." One aspect of it, Artificial Intelligence, is poised to change our lives dramatically. In this ongoing series, we will explore what the impact of AI will be on us as humans and designers. Our first installment, by Rob Girling, takes a look at what makes the stakes higher than ever before. Already, artificial intelligence is all around us, from self-driving cars and drones to virtual assistants and software that translate or invest.

From Driverless cars being rolled out recently to robots performing surgeries, technology has surely made a stellar progress. One such example is medical robots.Medical robots are set to revolutionize the healthcare sector, especially surgeries, since they are becoming more and more acceptable, globally. They not only help in reducing the time of surgery but also ensure the accuracy of the whole process coupled with better quality of patient care, thereby increasing the demand for surgical robots across the globe. Based on Product Medical Robots is segmented into Surgical Robots, Rehabilitation Robots, Non-invasive Radiosurgery Robots, Hospital and Pharmacy Robots. Surgical Robots is classified intoLaparoscopy Surgical Robotic Systems, Orthopedic Surgical Robotic Systems, Neurosurgical Robotic Systems, Steerable Robotic Catheters.

Many WhatsApp users are about to find themselves cut off from using the hugely popular chat app. Users of older iPhones and Android handsets are to find the app has stopped working after it said it would stop support from the end of the 2016. WhatsApp said that the move had been made to ensure that the app could continue to introduce new features and stay secure, which relies on the app being used on newer operating systems. But it has been criticised by many users, particularly those in developing markets where both the app and older handsets are popular. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Twitter might finally let people fix their bad tweets. The troubled company's CEO, Jack Dorsey, has suggested that he might have the site add an "edit" button so that updates can be fixed after they are posted. It was just one of a range of new year's resolutions that he made after taking requests from its users. Mr Dorsey admitted that the site needs such an edit button, but said also that he will need to think more about how it can be rolled out. The suggestion came after Mr Dorsey sent out a tweet asking users what they would want to see change about the site in 2017. A range of responses followed, including a number of people who asked for tweets to be allowed to be changed once they had been posted.