GE's electronic work instructions with the Google Glass by Novotek: Novotek combined GE's electronic work instructions with the Google Glass wearable computing device, and was demonstrated to Summit attendees in the Technology Fair. Novotek, is the largest European distributor for GE's Intelligent Platforms business and it has been awarded the Scanautomatic Prize for Innovation at Scanautomatic 2014 in Gothenburg, Sweden in October. Novotek has since 1986 worked with integration of IT and automation systems in the process and production industry. Novotek mainly supplies world-leading products and solutions from GE in the Nordics and Benelux. A work process management solution, GE's Proficy Workflow software provides users with interactive, step-by-step task instructions and captures process, traceability and quality data across systems to reduce errors, waste and delays.

Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalization or multi-user detection functions within the MF subgraph. In this paper, we show that the MF approximation is not particularly effective for multi-signal detection. We develop a new factor graph construction for application of the BP-MF framework to problems involving the detection of multiple signals. We then develop a low-complexity variant to the proposed construction in which Gaussian BP is applied to the equalization factors. In this case, the factor graph of the joint probability distribution is divided into three subgraphs: (i) a MF subgraph comprised of the observation factors and channel estimation, (ii) a Gaussian BP subgraph which is applied to multi-signal detection, and (iii) a discrete BP subgraph which is applied to demodulation and decoding. Expectation propagation is used to approximate discrete distributions with a Gaussian distribution and links the discrete BP and Gaussian BP subgraphs. The result is a probabilistic receiver architecture with strong theoretical justification which can be applied to multi-signal detection.

In magnetoencephalography (MEG) the conventional approach to source reconstruction is to solve the underdetermined inverse problem independently over time and space. Here we present how the conventional approach can be extended by regularizing the solution in space and time by a Gaussian process (Gaussian random field) model. Assuming a separable covariance function in space and time, the computational complexity of the proposed model becomes (without any further assumptions or restrictions) $\mathcal{O}(t^3 + n^3 + m^2n)$, where $t$ is the number of time steps, $m$ is the number of sources, and $n$ is the number of sensors. We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.

The variational autoencoder (VAE) is a generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes. We first elaborate Gaussian VAE, approximating the local covariance matrix of the decoder as an outer product of the principal direction at a position determined by a sample drawn from Gaussian distribution. We show that this model, referred to as VAE-ROC, better captures the data manifold, compared to the standard Gaussian VAE where independent multivariate Gaussian was used to model the decoder. Then we extend the VAE-ROC to handle mixed categorical and continuous data. To this end, we employ Gaussian copula to model the local dependency in mixed categorical and continuous data, leading to {\em Gaussian copula variational autoencoder} (GCVAE). As in VAE-ROC, we use the rank-one approximation for the covariance in the Gaussian copula, to capture the local dependency structure in the mixed data. Experiments on various datasets demonstrate the useful behaviour of VAE-ROC and GCVAE, compared to the standard VAE.

Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model. This gives us a flexible formalism for multi-view learning where the latent variables can be used both for exploratory purposes and for learning representations that enable efficient inference for ambiguous estimation tasks. Learning is performed in a Bayesian manner through the formulation of a variational compression scheme which gives a rigorous lower bound on the log likelihood. Our Bayesian framework provides strong regularization during training, allowing the structure of the latent space to be determined efficiently and automatically. We demonstrate this by producing the first (to our knowledge) published results of learning from dozens of views, even when data is scarce.

Variational inference is a powerful tool for approximate inference, and it has been recently applied for representation learning with deep generative models. We develop the variational Gaussian process (VGP), a Bayesian nonparametric variational family, which adapts its shape to match complex posterior distributions. The VGP generates approximate posterior samples by generating latent inputs and warping them through random non-linear mappings; the distribution over random mappings is learned during inference, enabling the transformed outputs to adapt to varying complexity. We prove a universal approximation theorem for the VGP, demonstrating its representative power for learning any model. For inference we present a variational objective inspired by auto-encoders and perform black box inference over a wide class of models. The VGP achieves new state-of-the-art results for unsupervised learning, inferring models such as the deep latent Gaussian model and the recently proposed DRAW.

The density matrices are positively semi-definite Hermitian matrices of unit trace that describe the state of a quantum system. The goal of the paper is to develop minimax lower bounds on error rates of estimation of low rank density matrices in trace regression models used in quantum state tomography (in particular, in the case of Pauli measurements) with explicit dependence of the bounds on the rank and other complexity parameters. Such bounds are established for several statistically relevant distances, including quantum versions of Kullback-Leibler divergence (relative entropy distance) and of Hellinger distance (so called Bures distance), and Schatten $p$-norm distances. Sharp upper bounds and oracle inequalities for least squares estimator with von Neumann entropy penalization are obtained showing that minimax lower bounds are attained (up to logarithmic factors) for these distances.

Abstract--A denoising algorithm seeks to remove noise, errors, or perturbations from a signal. Extensive research has been devoted to this arena over the last several decades, and as a result, todays denoisers can effectively remove large amounts of additive white Gaussian noise. A compressed sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired using a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively employ a generic denoiser in a CS reconstruction algorithm? In response, we develop an extension of the approximate message passing (AMP) framework, called Denoising-based AMP (DAMP), that can integrate a wide class of denoisers within its iterations. We demonstrate that, when used with a high performance denoiser for natural images, DAMP offers state-of-the-art CS recovery performance while operating tens of times faster than competing methods. We explain the exceptional performance of DAMP by analyzing some of its theoretical features. A key element in DAMP is the use of an appropriate Onsager correction term in its iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove. The fundamental challenge faced by a compressed sensing (CS) reconstruction algorithm is to reconstruct a highdimensional signal from a small number of measurements. In a single pixel camera, Φ might be a sequence of 1s and 0s representing the modulation of a micromirror array [3]. " Ψu with sparse u, where Ψ represents the inverse transform matrix. C. Metzler and R. Baraniuk are with the Department of Electrical and Computer Engineering, Rice University, Houston, TX 77023 USA (email: chris.metzler@rice.edu and richb@rice.edu). A. Maleki is with the Department of Statistics, Columbia University, New York, NY 10023 USA (email: arian@stat.columbia.edu). The work of C. Metzler supported by the NSF GRF Program and the DoD NDSEG Program. The work of A. Maleki was supported by the grant NSF CCF-1420328. However, when dealing with large signals, such as images, these convex programs are extremely computationally demanding. Therefore, lower cost iterative algorithms were developed; including matching pursuit [6], orthogonal matching pursuit [7], iterative hard-thresholding [8], compressive sampling matching pursuit [9], approximate message passing [10], and iterative soft-thresholding [11]-[16], to name just a few. See [17], [18] for a complete set of references. Here, δ " m{n is a measure of the under-determinacy of the problem, x y denotes the average of a vector, and The role of this term is illustrated in Figure 1. A QQplot is a visual inspection tool for checking the Gaussianity of the data. In a QQplot, deviation from a straight line is an evidence of non-Gaussianity.

In keeping with its vision of an era of cognitive computing enabled by acceleration technology, IBM Research (NYSE: IBM) today announced plans for a multi-year collaboration with the University of Illinois Urbana-Champaign to create the Center for Cognitive Computing Systems Research (C3SR) which will be housed within the College of Engineering on the Urbana campus. IBM has big ambitions for the center: "C3SR will build and optimize integrated systems such as state-of-the-art cognitive computing systems modeled on IBM's Watson technology that can master a subject area by learning from multimedia and multi-modal educational content. Such systems will efficiently ingest vast amounts of data including videos, lecture notes, homework, and textbooks, and reason through this knowledge effectively enough to be able to eventually pass a college level exam." Many details are yet to be worked out. The level of funding and size of installation will be announced this summer when the new center formally opens, said Hillery Hunter, a project driver and the director for systems acceleration and memory at IBM Research.

One of these target audiences is university students(undergraduate or graduate) learning about machine learning, including those who are beginning a career in deep learning and artificial intelligence research. The other target audience is software engineers who do not have a machine learning or statistics background, but want to rapidly acquire one and begin using deep learning in their product or platform. Basically, if you are interested in reading this book and haven't been turned off by the content of this post, the book is likely for you. The book starts off covering the required background for understanding later material, along with historical context and elementary explanations of the technical concepts. In fact, the entire first part of the book is dedicated to building the technical foundation required to study deep learning.