Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is on the computer network and the other is on the shared memory system. We establish an ergodic convergence rate $O(1/\sqrt{K})$ for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by $\sqrt{K}$ ($K$ is the total number of iterations). Our results generalize and improve existing analysis for convex minimization.

Measurement error in the observed values of the variables can greatly change the output of various causal discovery methods. This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance. In this paper, we study precise sufficient identifiability conditions for the measurement-errorfree causal model and show what information of the causal model can be recovered from observed data. In particular, we present two different sets of identifiability conditions, based on the second-order statistics and higher-order statistics of the data, respectively. The former was inspired by the relationship between the generating model of the measurement-errorcontaminated data and the factor analysis model, and the latter makes use of the identifiability result of the over-complete independent component analysis problem.

Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems (e.g. inpainting of missing pixels or zooming). Secondly it can deal with signal dependent noise models, particularly suited to digital cameras. As such, the scheme is especially adapted to computational photography. In order to illustrate this point, we provide an application to high dynamic range imaging from a single image taken with a modified sensor, which shows the effectiveness of the proposed scheme.

Combining abstract, symbolic reasoning with continuous neural reasoning is a grand challenge of representation learning. As a step in this direction, we propose a new architecture, called neural equivalence networks, for the problem of learning continuous semantic representations of algebraic and logical expressions. These networks are trained to represent semantic equivalence, even of expressions that are syntactically very different. The challenge is that semantic representations must be computed in a syntax-directed manner, because semantics is compositional, but at the same time, small changes in syntax can lead to very large changes in semantics, which can be difficult for continuous neural architectures. We perform an exhaustive evaluation on the task of checking equivalence on a highly diverse class of symbolic algebraic and boolean expression types, showing that our model significantly outperforms existing architectures.

Softmax Regression (synonyms: Multinomial Logistic, Maximum Entropy Classifier, or just Multi-class Logistic Regression) is a generalization of logistic regression that we can use for multi-class classification (under the assumption that the classes are mutually exclusive). In contrast, we use the (standard) Logistic Regression model in binary classification tasks. Now, let me briefly explain how that works and how softmax regression differs from logistic regression. Now, this softmax function computes the probability that this training sample x(i) belongs to class j given the weight and net input z(i). So, we compute the probability p(y j x(i); wj) for each class label in j 1, ..., k.

Artificial intelligence could increase South America's gross domestic product (GDP) growth by at least one percentage point annually by 2035, according to Accenture Research's report How artificial intelligence can accelerate South America's growth. The report calls attention to this technology's potential to transform the region's labor market and create a new relationship between humans and computers. In Brazil, for example, the study noted that the so-called gross value added (GVA) could increase by a total of $432 billion in this 17.5 year period. In terms of banks, the study reports that seven in ten bank users trust the financial guidance provided by automated systems. Some 39% of users consider the use of robo advisor platforms an agile tool and 31% see it as a way to lower costs for both banks and their customers.

The same report indicated a significant shift in investments by Chinese VCs, particularly in the Artificial Intelligence domain. It is believed that in five years' time, every company should be prepared to compete in the AI domain to survive. So are you aware that artificial intelligence can actually help marketers thrive? The power to understand customer needs, emotions, and preferences globally is a big boon of this emerging technology. Paradoxically enough, AI's greatest strength may be in creating a more personal experience for your customer.

Our AI Analytics is based on symbolic logic and propositional calculus. In other words, our algorithm discovers symbols that represent some level of importance based on propositional logic to drive a causational model. The causational model seeks out supporting context surrounding these situations. Thus, for each of the points, we expect AI to tell us the rationale. In a nutshell, the AI part of the analysis is to read the transcript like a human researcher and bring out positive points, negative points, and points with both positive and negative aspects. It does so in an objective way using Meta-Vision.

Global Artificial Intelligence Software Market Research reports 2017 tracks the major market events including product launches, technological developments, mergers & acquisitions, and the innovative business strategies opted by key market players. Along with strategically analyzing the key micro markets, the report also focuses on industry-specific drivers, restraints, opportunities and challenges in the Copper Strips market. This research report offers in-depth analysis of the market size (revenue), market share, major market segments, and different geographic regions, forecast for the next five years, key market players, and premium industry trends. It also focuses on the key drivers, restraints, opportunities and challenges. Leading companies operating in the global Artificial Intelligence Software market profiled in the report are: H2O, Braina, nanoRep, Reach Accountant, IPsoft.

How will AI change healthcare? "I'm sorry, you have cancer." These are words NO one wants to hear. When my father-in-law was diagnosed with cancer, I truly wish he'd had access to the amazing healthcare possibilities enabled by AI. Incredibly, a flawed second opinion probably saved his life--and nearly killed him.