SYS-CON Events announced today that MobiDev, a client-oriented software development company, will exhibit at SYS-CON's 21st International Cloud Expo, which will take place October 31-November 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. MobiDev is a software company that develops and delivers turn-key mobile apps, websites, web services, and complex software systems for startups and enterprises. Since 2009 it has grown from a small group of passionate engineers and business managers to a full-scale mobile software company with over 200 developers, designers, quality assurance engineers, project managers in house, specializing in the world-class mobile and web development. The company's main focus is saving the time and efforts of clients by providing full-cycle product development, offering dedicated project teams for long-term collaborations, making quality assurance an integral part of development, and regular product deliveries. Another major work principle embraced by MobiDev is flexibility: the company adjusts its activities to clients' business processes, tools, and environments; enables transparency of actions and quick problem-solving; and establishes effective communication with other involved teams, if required.

Why propensity scores should not be used Gary King, Richard Nielson.

Wizer's artificial intelligence technology can perform the work of human consumer research experts in building and analyzing consumer research, providing their customers with fast and affordable market insights AI-based SaaS consumer research company Wizer has announced the completion of the company's $4 million Series A Round. The funding was led by SOMV (State of Mind Ventures), a venture capital fund focusing on technology infrastructure. Nielsen Innovate, the early stage investment arm of Nielsen, participated in this round as well. To date, the company has raised $5.5 million, including $1.5 million in seed funding from YJ2, an investment fund backed by the Yahoo Japan Corporation, Nielsen Innovate, Barinboim Group, and the Israel Chief Scientist. Wizer provides marketers and agencies with comprehensive end-to-end market research solutions for brand positioning, product and concept testing, advertising effectiveness measurement, and more, utilizing proprietary artificial intelligence technology to deliver insightful client-ready research, quickly and affordably.

Bring your laptop, learn how to use OpenML in tutorials, and create something great that pushes the scientific community (and yourself) forward. Anything goes, from a cool extension of OpenML itself to solving a data-driven problem in your scientific domain. We also offer tutorials and inspirational invited talks.

The rapid growth of Electronic Health Records (EHRs), as well as the accompanied opportunities in Data-Driven Healthcare (DDH), has been attracting widespread interests and attentions. Recent progress in the design and applications of deep learning methods has shown promising results and is forcing massive changes in healthcare academia and industry, but most of these methods rely on massive labeled data. In this work, we propose a general deep learning framework which is able to boost risk prediction performance with limited EHR data. Our model takes a modified generative adversarial network namely ehrGAN, which can provide plausible labeled EHR data by mimicking real patient records, to augment the training dataset in a semi-supervised learning manner. We use this generative model together with a convolutional neural network (CNN) based prediction model to improve the onset prediction performance. Experiments on two real healthcare datasets demonstrate that our proposed framework produces realistic data samples and achieves significant improvements on classification tasks with the generated data over several stat-of-the-art baselines.

In this note, we point out a basic link between generative adversarial (GA) training and binary classification -- any powerful discriminator essentially computes an (f-)divergence between real and generated samples. The result, repeatedly re-derived in decision theory, has implications for GA Networks (GANs), providing an alternative perspective on training f-GANs by designing the discriminator loss function.

Conditional independence testing is a fundamental problem underlying causal discovery and a particularly challenging task in the presence of nonlinear and high-dimensional dependencies. Here a fully non-parametric test for continuous data based on conditional mutual information combined with a local permutation scheme is presented. Through a nearest neighbor approach, the test efficiently adapts also to non-smooth distributions due to strongly nonlinear dependencies. Numerical experiments demonstrate that the test reliably simulates the null distribution even for small sample sizes and with high-dimensional conditioning sets. The test is better calibrated than kernel-based tests utilizing an analytical approximation of the null distribution, especially for non-smooth densities, and reaches the same or higher power levels. Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power. For smaller sample sizes and lower dimensions, the test is faster than random fourier feature-based kernel tests if the permutation scheme is (embarrassingly) parallelized, but the runtime increases more sharply with sample size and dimensionality. Thus, more theoretical research to analytically approximate the null distribution and speed up the estimation for larger sample sizes is desirable.

Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability distribution functions. With this modeling strategy, we identify sub-labels and generate synthetic data in order to reach better classification accuracy. It means we focus on increasing the training data synthetically to increase the classification accuracy.

When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode. A possible mitigation of such events is to slow down the learning process. This paper presents a novel approach to control the SGD learning rate, that uses two statistical tests. The first one, aimed at fast learning, compares the momentum of the normalized gradient vectors to that of random unit vectors and accordingly gracefully increases or decreases the learning rate. The second one is a change point detection test, aimed at the detection of catastrophic learning episodes; upon its triggering the learning rate is instantly halved. Both abilities of speeding up and slowing down the learning rate allows the proposed approach, called SALeRA, to learn as fast as possible but not faster. Experiments on standard benchmarks show that SALeRA performs well in practice, and compares favorably to the state of the art.

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning. The proposed framework learns classifier from each hypothetical labeling, and searches for the optimal labeling by minimizing the generalization error of the learned classifiers associated with the hypothetical labeling. Kernel classifier is employed in our framework. By generalization analysis via Rademacher complexity, the generalization error bound for the kernel classifier learned from hypothetical labeling is expressed as the sum of pairwise similarity between the data from different classes, parameterized by the weights of the kernel classifier. Such pairwise similarity serves as the discriminative similarity for the purpose of clustering and semi-supervised learning, and discriminative similarity with similar form can also be induced by the integrated squared error bound for kernel density classification. Based on the discriminative similarity induced by the kernel classifier, we propose new clustering and semi-supervised learning methods. 1 Y. Yang et al.