This paper studies single-image depth perception in the wild, i.e., recovering depth from a single image taken in unconstrained settings. We introduce a new dataset "Depth in the Wild" consisting of images in the wild annotated with relative depth between pairs of random points. We also propose a new algorithm that learns to estimate metric depth using annotations of relative depth. Compared to the state of the art, our algorithm is simpler and performs better. Experiments show that our algorithm, combined with existing RGB-D data and our new relative depth annotations, significantly improves single-image depth perception in the wild.

We propose using canonical correlation analysis (CCA) to generate features from sequences of medical billing codes. Applying this novel use of CCA to a database of medical billing codes for patients with diverticulitis, we first demonstrate that the CCA embeddings capture meaningful relationships among the codes. We then generate features from these embeddings and establish their usefulness in predicting future elective surgery for diverticulitis, an important marker in efforts for reducing costs in healthcare.

We introduce a multivariate stochastic volatility model for asset returns that imposes no restrictions to the structure of the volatility matrix and treats all its elements as functions of latent stochastic processes. When the number of assets is prohibitively large, we propose a factor multivariate stochastic volatility model in which the variances and correlations of the factors evolve stochastically over time. Inference is achieved via a carefully designed feasible and scalable Markov chain Monte Carlo algorithm that combines two computationally important ingredients: it utilizes invariant to the prior Metropolis proposal densities for simultaneously updating all latent paths and has quadratic, rather than cubic, computational complexity when evaluating the multivariate normal densities required. We apply our modelling and computational methodology to $571$ stock daily returns of Euro STOXX index for data over a period of $10$ years. MATLAB software for this paper is available at http://www.aueb.gr/users/mtitsias/code/msv.zip.

Abstract--Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer . The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the better adapted distance or similarity measures. Here, we address the problem of learning these transformations when the underlying constraint generation process is nonstationary. This nonstationarity can be due to changes in either the ground-truth clustering used to generate constraints or changes in the feature subspaces in which the class structure is apparent. We propose Online Convex Ensemble StrongLy Adaptive Dynamic Learning (OCELAD), a general adaptive, online approach for learning and tracking optimal metrics as they change over time that is highly robust to a variety of nonstationary behaviors in the changing metric. We apply the OCELAD framework to an ensemble of online learners. Specifically, we create a retro-initialized composite objective mirror descent (COMID) ensemble (RICE) consisting of a set of parallel COMID learners with different learning rates, and demonstrate parameter-free RICE-OCELAD metric learning on both synthetic data and a highly nonstationary Twitter dataset. We show significant performance improvements and increased robustness to nonstationary effects relative to previously proposed batch and online distance metric learning algorithms. He effectiveness of many machine learning and data mining algorithms depends on an appropriate measure of pairwise distance between data points that accurately reflects the learning task, e.g., prediction, clustering or classification. The kNN classifier, K-means clustering, and the Laplacian-SVM semi-supervised classifier are examples of such distance-based machine learning algorithms. In settings where there is clean, appropriately-scaled spherical Gaussian data, standard Euclidean distance can be utilized. However, when the data is heavy tailed, multimodal, or contaminated by outliers, observation noise, or irrelevant or replicated features, use of Euclidean inter-point distance can be problematic, leading to bias or loss of discriminative power.

We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is represented by an electronic density function. A multiscale invariant dictionary is calculated with wavelet scattering invariants. It cascades a first wavelet transform which separates scales, with a second wavelet transform which computes interactions across scales. Sparse scattering regressions give state of the art results over two databases of organic planar molecules. On these databases, the regression error is of the order of the error produced by DFT codes, but at a fraction of the computational cost.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

Machine learning uses algorithms that learn from data to help make better decisions. Examples of machine learning applications include clustering, where objects are grouped into bins with similar traits;regression, where relationships among variables are estimated; and classification, where a trained model is used to predict a categorical response. Let's take a look at the steps in a machine learning workflow. You might have data in many places, such as multiple spreadsheets and databases. MATLAB provides interactive tools that make it easy to perform a variety of machine learning tasks, including connecting to and importing data.

The 1st Workshop on Neural Machine Translation is a new annual workshop that will be co-located with ACL 2017 (Vancouver, July 30-August 4, 2017). Neural Machine Translation (NMT) is a simple new architecture for getting machines to learn to translate. Despite being relatively recent, NMT has demonstrated promising results and attracted much interest, achieving state-of-the-art results on a number of shared tasks. This workshop aims to cultivate research in neural machine translation and other aspects of machine translation and multilinguality that utilize neural models.

"At Altek, we are constantly striving to enhance our digital image solutions and set the direction for the future of smarter imaging devices," said Jason Lin, General Manager and Corporate Senior Vice President of Altek. "CEVA's imaging and vision DSP provides the platform which allows us to further enhance the image quality of our solutions and push the boundaries of what a camera can do using artificial intelligence and advanced vision algorithms." "Altek is a proven leader in imaging, with a strong track record in the smartphone space and we are excited to work with them," said, Ilan Yona, vice president and general manager of CEVA's Vision Business Unit. "The combination of Altek's advanced imaging technologies along with our DSP-based vision and machine learning offering creates one of the most intelligent digital imaging solutions on the market today." CEVA's latest generation imaging and vision DSP platforms address the extreme processing requirements and low power constraints of the most sophisticated machine learning and machine vision applications used in smartphones, surveillance, augmented reality, sense and avoid drones and self-driving cars.

It was a Saturday afternoon. I met up with a friend to enjoy the occasional good weather by the lake. It was a day void of serious topics or stress. Life is good, I thought to myself. I headed home after a few drinks to prepare for an upcoming trip.