In recent years, advances in machine learning algorithms, cheap computational resources, and the availability of big data have spurred the deep learning revolution in various application domains. In particular, supervised learning techniques in image analysis have led to superhuman performance in various tasks, such as classification, localization, and segmentation, while unsupervised learning techniques based on increasingly advanced generative models have been applied to generate high-resolution synthetic images indistinguishable from real images. In this paper we consider a state-of-the-art machine learning model for image inpainting, namely a Wasserstein Generative Adversarial Network based on a fully convolutional architecture with a contextual attention mechanism. We show that this model can successfully be transferred to the setting of digital elevation models (DEMs) for the purpose of generating semantically plausible data for filling voids. Training, testing and experimentation is done on GeoTIFF data from various regions in Norway, made openly available by the Norwegian Mapping Authority.

StarCraft, one of the most popular real-time strategy games, is a compelling environment for artificial intelligence research for both micro-level unit control and macro-level strategic decision making. In this study, we address an eminent problem concerning macro-level decision making, known as the 'fog-of-war', which rises naturally from the fact that information regarding the opponent's state is always provided in the incomplete form. For intelligent agents to play like human players, it is obvious that making accurate predictions of the opponent's status under incomplete information will increase its chance of winning. To reflect this fact, we propose a convolutional encoder-decoder architecture that predicts potential counts and locations of the opponent's units based on only partially visible and noisy information. To evaluate the performance of our proposed method, we train an additional classifier on the encoder-decoder output to predict the game outcome (win or lose). Finally, we designed an agent incorporating the proposed method and conducted simulation games against rule-based agents to demonstrate both effectiveness and practicality. All experiments were conducted on actual game replay data acquired from professional players.

Convolutional Neural Networks (CNNs) can learn effective features, though have been shown to suffer from a performance drop when the distribution of the data changes from training to test data. In this paper we analyze the internal representations of CNNs and observe that the representations of unseen data in each class, spread more (with higher variance) in the embedding space of the CNN compared to representations of the training data. More importantly, this difference is more extreme if the unseen data comes from a shifted distribution. Based on this observation, we objectively evaluate the degree of representation's variance in each class via eigenvalue decomposition on the within-class covariance of the internal representations of CNNs and observe the same behaviour. This can be problematic as larger variances might lead to mis-classification if the sample crosses the decision boundary of its class. We apply nearest neighbor classification on the representations and empirically show that the embeddings with the high variance actually have significantly worse KNN classification performances, although this could not be foreseen from their end-to-end classification results. To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN's representation, improving performance on unseen test data from a shifted distribution. We empirically evaluate DWCCA on two datasets for Acoustic Scene Classification (DCASE2016 and DCASE2017). We demonstrate that not only does DWCCA significantly improve the network's internal representation, it also increases the end-to-end classification accuracy, especially when the test set exhibits a distribution shift. By adding DWCCA to a VGG network, we achieve around 6 percentage points improvement in the case of a distribution mismatch.

Lots of people say that neural nets are a "black box" whose successful predictions are impossible to explain. I hate treating anything as a black box, it grinds against my curious nature. It's also not a very helpful mental model -- understanding what neural nets are and how they arrive at the conclusions they do can help practitioners gain insight into using them. Viewed through the proper lens, neural nets' prediction making capability makes a lot of sense. This article is about taking nets out of their black box by understanding what a neural network really represents.

I took a Deep Learning course through The Bradfield School of Computer Science in June. This series is a journal about what I learned in class, and what I've learned since. This is the first article in this series, and is is about the recommended preparation for the Deep Learning course and what we learned in the first class. Although normally the "prework" comes before the introduction, I'm going to give the 30,000 foot view of the fields of artificial intelligence, machine learning, and deep learning at the top. I have found that this context can really help us understand why the prerequisites seem so broad, and help us study just the essentials. Besides, the history and landscape of artificial intelligence is interesting, so lets dive in!

If you've ever wanted to get your hands on the Mona Lisa, you may just be in luck. For researchers have found a way for art fanatics to create their own version of a priceless masterpiece, through a combination of AI and 3D printing. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint'. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint' (pictured) Despite the progress so far, the team says they have a few improvements to make before they can whip up a dazzling dupe of'Starry Night.' 'If you just reproduce the colour of a painting as it looks in the gallery, it might look different in your home,' says Changil Kim, one of the researchers from MIT that published a paper on the system, which will be presented in December. 'Our system works under any lighting condition, which shows a far greater colour reproduction capability than almost any other previous work.'

It already has quite a few smart code confections: Rekognition, Lex, Polly, Transcribe, Comprehend, Translate, Sagemaker, and Greengrass, among others. At its re:Invent gathering in Las Vegas today, AWS threw a handful of new flavors into the mix, among them: Elastic Inference, SageMaker GroundTruth, SageMaker RL, Amazon SageMaker Neo, Personalize, Forecast, Textract, and Comprehend Medical. It also teased a machine-learning inference chip called Inferentia, and a small radio-controlled car called DeepRacer for executing autonomous driving models in the real-world and terrifying pets. It's a 1/18th scale race car that's ostensibly intended to help people understand and implement reinforcement learning. It may also help with customer acquisition, retention, and spending.

Machine learning, and more specifically deep learning, have shown remarkable performance in sensing, communications, and inference. In this paper, we consider the application of the deep unfolding technique in the problem of signal reconstruction from its one-bit noisy measurements. Namely, we propose a model-based machine learning method and unfold the iterations of an inference optimization algorithm into the layers of a deep neural network for one-bit signal recovery. The resulting network, which we refer to as DeepRec, can efficiently handle the recovery of high-dimensional signals from acquired one-bit noisy measurements. The proposed method results in an improvement in accuracy and computational efficiency with respect to the original framework as shown through numerical analysis.

Among the plethora of techniques devised to curb the prevalence of noise in medical images, deep learning based approaches have shown the most promise. However, one critical limitation of these deep learning based denoisers is the requirement of high-quality noiseless ground truth images that are difficult to obtain in many medical imaging applications such as X-rays. To circumvent this issue, we leverage recently proposed approach of [7] that incorporates Stein's Unbiased Risk Estimator (SURE) to train a deep convolutional neural network without requiring denoised ground truth X-ray data. Our experimental results demonstrate the effectiveness of SURE based approach for denoising X-ray images.

The goal of this document is to provide a pedagogical introduction to the main concepts underpinning the training of deep neural networks using gradient descent; a process known as backpropagation. Although we focus on a very influential class of architectures called "convolutional neural networks" (CNNs) the approach is generic and useful to the machine learning community as a whole. Motivated by the observation that derivations of backpropagation are often obscured by clumsy index-heavy narratives that appear somewhat mathemagical, we aim to offer a conceptually clear, vectorized description that articulates well the higher level logic. Following the principle of "writing is nature's way of letting you know how sloppy your thinking is", we try to make the calculations meticulous, self-contained and yet as intuitive as possible. Taking nothing for granted, ample illustrations serve as visual guides and an extensive bibliography is provided for further explorations. (For the sake of clarity, long mathematical derivations and visualizations have been broken up into short "summarized views" and longer "detailed views" encoded into the PDF as optional content groups. Some figures contain animations designed to illustrate important concepts in a more engaging style. For these reasons, we advise to download the document locally and open it using Adobe Acrobat Reader. Other viewers were not tested and may not render the detailed views, animations correctly.)