Nvidia and VMware on Monday announced a new software product that lets customers virtualize GPUs, either on premise or as part of VMware Cloud on AWS. The companies say it's the first hybrid cloud offering that lets enterprises use GPUs to accelerate AI, machine learning or deep learning workloads. "In a modern data center, organizations are going to be using GPUs to power AI, deep learning, analytics," John Fanelli, VP of product management for Nvidia, told reporters. "And due to the scale of those types of workloads, they're going to be doing some processing on premise in data centers, some processing in clouds and continually iterating between them." The new offering starts with the enterprise data center product -- Nvidia's new Virtual Compute Server (vComputeServer) software.

Attempts from different disciplines to provide a fundamental understanding of deep learning have advanced rapidly in recent years, yet a unified framework remains relatively limited. In this article, we provide one possible way to align existing branches of deep learning theory through the lens of dynamical system and optimal control. By viewing deep neural networks as discrete-time nonlinear dynamical systems, we can analyze how information propagates through layers using mean field theory. When optimization algorithms are further recast as controllers, the ultimate goal of training processes can be formulated as an optimal control problem. In addition, we can reveal convergence and generalization properties by studying the stochastic dynamics of optimization algorithms. This viewpoint features a wide range of theoretical study from information bottleneck to statistical physics. It also provides a principled way for hyper-parameter tuning when optimal control theory is introduced. Our framework fits nicely with supervised learning and can be extended to other learning problems, such as Bayesian learning, adversarial training, and specific forms of meta learning, without efforts. The review aims to shed lights on the importance of dynamics and optimal control when developing deep learning theory.

Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web daily. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose a deep learning model HTMLPhish based on the HTML analysis of a web page for accurate phishing attack detection. By using our proposed HTMLPhish, the experimental results on a dataset of over 300,000 web pages yielded 97.2% accuracy, which significantly outperforms the traditional machine learning methods such as Support Vector Machine, Random Forest and Logistics Regression. We also show the advantage of HTMLPhish in the aspect of the temporal stability and robustness by testing our proposed model on a dataset collected after two months when the model was trained. In addition, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.

--Incorporating deep neural networks in image com-pressive sensing (CS) receives intensive attentions recently. As deep network approaches learn the inverse mapping directly from the CS measurements, a number of models have to be trained, each of which corresponds to a sampling rate. This may potentially degrade the performance of image CS, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement of the model is attributed to block-based sampling rates allocation and model-level removal of blocking artifacts. Specifically, the image blocks with a variety of sampling rates can be reconstructed in a single model by exploiting inter-block correlation. At the same time, the initially reconstructed blocks are reassembled into a full image to remove blocking artifacts within the network by unrolling a hand-designed block-based CS algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics, PSNR, SSIM, and subjective visual quality. Compressive sensing (CS), an emerging sampling and reconstructing strategy, can recover original signal from dramatically fewer measurements with a sub-Nyquist sampling rate [1]. As CS has the potentials of significantly improving the sampling speed and sensor energy efficiency, it has been applied in many practical applications, including single pixel imaging [2], fast magnetic resonance imaging [3], high-speed video cameras [4] and image encryption [5]. To deal with high-dimensional natural images efficiently, block-based CS is proposed as a lightweight CS approach [6]-[8]. In such strategy, a scene under view is partitioned into some small blocks, which are then sampled and reconstructed independently. Meaningful information is usually not uniformly distributed in an image, so the block partition benefits more fair allocation of the sensing resources for the whole image [9]. This work was supported by the National Natural Science Foundation of China (no.

While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domains such as those arising in applications like social network analysis. Such domains rely on relational representations to capture complex relationships between entities and their attributes. Thus, we consider the problem of learning neural networks for relational data. We distinguish ourselves from current approaches that rely on expert hand-coded rules by learning relational random-walk-based features to capture local structural interactions and the resulting network architecture. We further exploit parameter tying of the network weights of the resulting relational neural network, where instances of the same type share parameters. Our experimental results across several standard relational data sets demonstrate the effectiveness of the proposed approach over multiple neural net baselines as well as state-of-the-art statistical relational models.

Generating paraphrases from given sentences involves decoding words step by step from a large vocabulary. To learn a decoder, supervised learning which maximizes the likelihood of tokens always suffers from the exposure bias. Although both reinforcement learning (RL) and imitation learning (IL) have been widely used to alleviate the bias, the lack of direct comparison leads to only a partial image on their benefits. In this work, we present an empirical study on how RL and IL can help boost the performance of generating paraphrases, with the pointer-generator as a base model. Experiments on the benchmark datasets show that (1) imitation learning is constantly better than reinforcement learning; and (2) the pointer-generator models with imitation learning outperform the state-of-the-art methods with a large margin.

Aim: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring. Materials and Methods: The PubMed and MEDLINE databases were searched for articles published before September 2018 using relevant search terms. The search strategy focused on articles applying ML to high-grade glioma biomarkers for treatment response monitoring, prognosis, and prediction. Results: Magnetic resonance imaging (MRI) is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Using carefully chosen image features, ML is frequently used to allow accurate classification in a variety of scenarios. Rather than being chosen by human selection, ML also enables image features to be identified by an algorithm. Much research is applied to determining molecular profiles, histological tumour grade, and prognosis using MRI images acquired at the time that patients first present with a brain tumour. Differentiating a treatment response from a post-treatment-related effect using imaging is clinically important and also an area of active study (described here in one of two Special Issue publications dedicated to the application of ML in glioma imaging). Conclusion: Although pioneering, most of the evidence is of a low level, having been obtained retrospectively and in single centres. Studies applying ML to build neuro-oncology monitoring biomarker models have yet to show an overall advantage over those using traditional statistical methods. Development and validation of ML models applied to neuro-oncology require large, well-annotated datasets, and therefore multidisciplinary and multi-centre collaborations are necessary.

Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapefiles and raster images from USGS image archive were used for developing images for both training and testing. Feature Pyramid based Mask RCNN yields average class accuracy of 97.56% for both parking spaces and vehicles

Effective training of Deep Neural Networks requires massive amounts of data and compute. As a result, longer times are needed to train complex models requiring large datasets, which can severely limit research on model development and the exploitation of all available data. In this paper, this problem is investigated in the context of precipitation nowcasting, a term used to describe highly detailed short-term forecasts of precipitation and other hazardous weather. Convolutional Neural Networks (CNNs) are a powerful class of models that are well-suited for this task; however, the high resolution input weather imagery combined with model complexity required to process this data makes training CNNs to solve this task time consuming. To address this issue, a data-parallel model is implemented where a CNN is replicated across multiple compute nodes and the training batches are distributed across multiple nodes. By leveraging multiple GPUs, we show that the training time for a given nowcasting model architecture can be reduced from 59 hours to just over 1 hour. This will allow for faster iterations for improving CNN architectures and will facilitate future advancement in the area of nowcasting.

We introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of preprint articles. We explore fifty author-annotated categories and empirically motivate a task design of grouping 10.5 million annotated paragraphs into thirteen classes. We demonstrate that the task setup aligns with known success rates from the state of the art, peaking at a 0.91 F1-score via a BiLSTM encoder-decoder model. Additionally, we introduce a lexeme serialization for mathematical formulas, and observe that context-aware models could improve when also trained on the symbolic modality. Finally, we discuss the limitations of both data and task design, and outline potential directions towards increasingly complex models of scientific discourse, beyond isolated statements.