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Generally, default builds of popular deep learning frameworks like TensorFlow are not fully optimized for training and inference on CPU. To solve this issue, Intel has open-sourced framework optimizations for Intel Xeon processors. Microsoft recently announced a partnership with Intel to bring optimized deep learning frameworks to Azure. These optimizations are available on the Azure marketplace in the name of Intel Optimized Data Science VM for Linux (Ubuntu). These optimizations leverage the Intel Advanced Vector Extensions 512 (Intel AVX-512) and Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) to accelerate training and inference on Intel Xeon Processors.

Nvidia is all set to host its 10th annual GPU Technology Conference (Nvidia GTC 2019). Back when the company started it off in 2009, the focus was purely on solving computing problems through GPUs. However, over the years this has evolved into various other subjects. Nvidia GTC 2019 is all about Artificial Intelligence and Deep Learning. Let us take a closer look at the Nvidia GTC 2019 conference – at all the major names and discussions which make the GTC one of the best events of the year!

Machine learning algorithms using large datasets may be utilized to accurately estimate prognosis and guide therapy for patients with adult congenital heart disease, according to a study published in the European Heart Journal. The investigators of this large cohort, single-center study sought to examine the utility of machine learning algorithms as a prognostic model and to guide therapeutic decision-making in patients with adult congenital heart disease or pulmonary hypertension. The study sample included 10,019 adults under active follow-up at the Royal Brompton Hospital in London between 2000 and 2018. Patient data were retrospectively collected -- including clinical and demographic data, ECG parameters, cardiopulmonary exercise data, and laboratory markers -- and incorporated into deep learning algorithms. Specific deep learning models were then built for patient categorization into diagnostic subsets, disease complexity subsets, and by New York Heart Association (NYHA) class.

You can tell a lot about people from their Wi-Fi connections -- including, as it turns out, how many of them are standing near an access point. In a newly published research paper ("DeepCount: Crowd Counting with WiFi via Deep Learning") on the preprint server Arxiv.org, Their work comes not long after researchers at Ryerson University in Toronto demonstrated a neural network that can determine whether smartphone owners are walking, biking, or driving around a few city blocks by using Wi-Fi data, and after Purdue University researchers developed a system that uses Wi-Fi access logs to suss out relationships among users, locations, and activities. In this latest study, the team leveraged channel state information (CSI) -- specifically phase and amplitude -- to create a two-model system consisting of an activity recognition model and deep learning model. The deep learning model was tasked with correlating the number of people and channels by mapping those people's activities to CSI, while the former recognized when someone entered or left the room via an electronic switch.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.

Time Series forecasting is an important area of Machine Learning. It is important because there are so many prediction problems that involve a time component. However, while the time component adds additional information, it also makes time series problems more difficult to handle compared to many other prediction tasks. Deep Learning has plenty of applications in the world of statistical analysis. One of the areas with an environment for its applicability is the time series.

We don't know why deep learning forms of neural networks achieve great success on many tasks; the discipline has a paucity of theory to explain its empirical successes. As Facebook's Yann LeCun has said, deep learning is like the steam engine, which preceded the underlying theory of thermodynamics by many years. It would be the harbinger of an entirely new medium of calculation, harnessing the powers of subatomic particles to obliterate the barriers of time in solving incalculable problems. But some deep thinkers have been plugging away at the matter of theory for several years now. On Wednesday, the group presented a proof of deep learning's superior ability to simulate the computations involved in quantum computing.

Who hasn't heard of Intel, the tech giant setting the pace with its processors? While it used to lead the computing devices industry, its reputation was slowly being eclipsed lately due to competitors sprouting up, with processors for mobile and other next-generation devices. Fortunately, this tech leader does not plan on getting submerged anytime soon. That's right: Intel used to set the trend in computing, and it plans to do the same for Artificial Intelligence. The latest step in this endeavour: acquire Vertex.AI, as announced on 16th August 2018, and get this team to work alongside Intel's Movidius team.

To evaluate the performance, agreement, and efficiency of a fully convolutional network (FCN) for liver lesion detection and segmentation at CT examinations in patients with colorectal liver metastases (CLMs). This retrospective study evaluated an automated method using an FCN that was trained, validated, and tested with 115, 15, and 26 contrast material–enhanced CT examinations containing 261, 22, and 105 lesions, respectively. Manual detection and segmentation by a radiologist was the reference standard. Performance of fully automated and user-corrected segmentations was compared with that of manual segmentations. The interuser agreement and interaction time of manual and user-corrected segmentations were assessed. Analyses included sensitivity and positive predictive value of detection, segmentation accuracy, Cohen κ, Bland-Altman analyses, and analysis of variance. Automated detection and segmentation of CLM by using deep learning with convolutional neural networks, when manually corrected, improved efficiency but did not substantially change agreement on volumetric measurements. Supplemental material is available for this article. A deep learning method shows promise for facilitating detection and segmentation of colorectal liver metastases; user correction of three-dimensional automated segmentations can generally resolve deficiencies of fully automated segmentation for small metastases and is faster than manual three-dimensional segmentation. Per-lesion sensitivity for lesions smaller than 10 mm was very low with automated segmentation (0.10) but was higher for user-corrected segmentation (0.30–0.57) and manual segmentation (0.58–0.70).