Emirates NBD will also utilize AWS data analytics, Internet of Things (IoT), Natural Language Processing (NLP), and other advanced technologies as part of its ongoing efforts to better engage with customers and simplify banking. A front-runner in retail banking innovation, Emirates NBD is working with AWS because of its broad and deep portfolio of cloud services and the increased security and control Emirates NBD can achieve in the cloud, and is continuing to invest in AWS as its preferred provider for machine learning workloads. With AWS, Emirates NBD will take further advantage of AWS artificial intelligence and machine learning services including Amazon SageMaker, a fully managed machine learning service for building, training, and deploying machine learning models to provide relevant real-time banking experiences. To create a more rewarding and customer-centric banking experience, Emirates NBD is also leveraging Amazon Personalize, an AWS machine learning service that enables the development of individualized recommendations to launch new personalized retail banking applications. One of the first of these applications is a personal finance manager that uses an automated, self-learning system to deliver a highly personalized banking experience to customers in order to predict what each individual customer needs and match this with the most appropriate solution. To support this work, Emirates NBD is using Amazon Polly, a cloud service that uses advanced deep learning technologies to convert written content into human-like speech, in its automated call center to further enhance customer interactions by delivering lifelike voice banking experiences.

The Commonwealth Bank of Australia (CBA) has announced a trial of a redesigned banking app that it says has been backed by "world-leading" machine learning, data analytics, and behavioural science. The bank said its app boasts 5.3 million unique users and more than 6.5 million log-ons per day. It expects the redesign will provide the "first completely personalised and smart digital banking experience in Australia, backed by world-leading application of machine learning technology". According to chief digital officer Pete Steel, CBA has a "unique ability to use technology and innovation capabilities to support good financial habits". "We're using a combination of cutting edge machine learning technology, data analytics, and behavioural science to develop smart banking features to create a highly personalised digital banking experience," he said.

Perhaps you wanted to export the list of workspaces placed on Premium capacities, as in the following screenshot. Or you simply wanted to export all workspaces and track over time how many workspaces are added or deleted. Whatever the scenario, the Workspace page now provides an Export command so that you can export the current list to a comma-separated values (CSV) file. The same export to CSV feature is also available on the Embed Codes page.

Let's take this onslaught of information and clinically dissect it to get a clearer view of how the travel industry will be affected. We can broadly define the core aspects of the travel industry in three main categories: preparation, buying and the actual experience itself. Assume you want to go from New York to London on vacation. If you are bringing your family of four or five people, you will likely end up searching for hours on various search engines like Kayak or Expedia to get the right itinerary and number of stops, book the nearest airport, etc. This is a time-consuming and frustrating part of the vacation planning process.

Companies understand the importance of artificial intelligence and machine learning, especially since it's become an increasingly important competitive differentiator, and are eager to jump in. But as always, the question stands: Once you've identified the potential of AI for your business, do you buy, or do you build? That's one of the big questions we'll be tackling at this year's Transform: Accelerating Your Business With AI. Spoiler alert: Lyft, Uber, Airbnb, and LinkedIn, featuring prominent speakers at this year's event, have come down firmly on the side of building their own AI solutions. And they've ended up with some dramatically successful -- and very cool -- results. Of course, it's fine for tech companies in Silicon Valley with a ton of resources to build their own solutions.

Deep learning has become an area of interest in most scientific areas, including physical sciences. Modern networks apply real-valued transformations on the data. Particularly, convolutions in convolutional neural networks discard phase information entirely. Many deterministic signals, such as seismic data or electrical signals, contain significant information in the phase of the signal. We explore complex-valued deep convolutional networks to leverage non-linear feature maps. Seismic data commonly has a lowcut filter applied, to attenuate noise from ocean waves and similar long wavelength contributions. Discarding the phase information leads to low-frequency aliasing analogous to the Nyquist-Shannon theorem for high frequencies. In non-stationary data, the phase content can stabilize training and improve the generalizability of neural networks. While it has been shown that phase content can be restored in deep neural networks, we show how including phase information in feature maps improves both training and inference from deterministic physical data. Furthermore, we show that the reduction of parameters in a complex network results in training on a smaller dataset without overfitting, in comparison to a real-valued network with the same performance.

Deep neural networks (DNNs) are powerful tools in computer vision tasks. However, in many realistic scenarios label noise is prevalent in the training images, and overfitting to these noisy labels can significantly harm the generalization performance of DNNs. We propose a novel technique to identify data with noisy labels based on the different distributions of the predictive uncertainties from a DNN over the clean and noisy data. Additionally, the behavior of the uncertainty over the course of training helps to identify the network weights which best can be used to relabel the noisy labels. Data with noisy labels can therefore be cleaned in an iterative process. Our proposed method can be easily implemented, and shows promising performance on the task of noisy label detection on CIFAR-10 and CIFAR-100.

The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/advice or is just socializing. Our primary submission was ranked as the second one among all participants in the official evaluation phase. The article presents our primary solution: Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings. This is followed by a description of two contrastive solutions based on ensemble methods.

Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimensions)--such as in the case of clinical patient data. To address these challenges, we propose (1) a continuous-time version of the Gated Recurrent Unit, building upon the recent Neural Ordinary Differential Equations (Chen et al., 2018), and (2) a Bayesian update network that processes the sporadic observations. We bring these two ideas together in our GRU-ODE-Bayes method. We then demonstrate that the proposed method encodes a continuity prior for the latent process and that it can exactly represent the Fokker-Planck dynamics of complex processes driven by a multidimensional stochastic differential equation. Additionally, empirical evaluation shows that our method outperforms the state of the art on both synthetic data and real-world data with applications in healthcare and climate forecast. What is more, the continuity prior is shown to be well suited for low number of samples settings.

Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? Here we study the intrinsic dimensionality (ID) of data-representations, i.e. the minimal number of parameters needed to describe a representation. We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer. Across layers, the ID first increases and then progressively decreases in the final layers. Remarkably, the ID of the last hidden layer predicts classification accuracy on the test set. These results can neither be found by linear dimensionality estimates (e.g., with principal component analysis), nor in representations that had been artificially linearized. They are neither found in untrained networks, nor in networks that are trained on randomized labels. This suggests that neural networks that can generalize are those that transform the data into low-dimensional, but not necessarily flat manifolds.