Deep learning, often referred to as neural networks, is a type of machine learning where the algorithm is trained using a complex network of patterns similar to the brain's neural network. The methodology has demonstrated high performance in identifying disease from imaging studies, taking the methods of machine learning one step further. Rather than learning from a set of inputs given to the machine from the algorithm developer, the algorithm learns from the data. It is a more advanced kind of machine learning that requires large datasets to train the algorithm and the data must be standardized as the machine has to learn where to identify irregularities in images.

In non-invasive approaches such as electroencephalography (EEG), brain activity is measured with electrodes placed on the scalp, which has the advantage that no surgery is required. Decoding the recorded signals into useful real-time information is challenging, but advances in materials engineering and machine learning in the past decade are showing promise. In an Article in this issue, Yeo et al. demonstrate a compact and lightweight, scalp-wearable device that reads out visually evoked electrical signals with high resolution. A deep learning algorithm is trained to classify the signals and can be used offline. In one experiment (with able-bodied subjects) it is shown that a wheelchair can be controlled in real time, demonstrating the practical promise of this approach.

Deep learning is hot right now. Applications such as voice recognition, facial recognition, language translation, medical diagnostics, self-driving vehicles, and even the detection of credit fraud, are becoming more and more woven into the fabric of modern life. Because of such successes, and the opportunities they open up for further extensions of the technology, deep learning is currently one of the most active fields in computer science research, and progress has been rapid. In this article we'll take a brief look at several of the latest trends in deep learning research. Perhaps the area of deep learning research that has received the most public notice in recent years relates to the advent of driverless cars and trucks.

PyTorch has become one of the most popular deep learning frameworks in the market and certainly a favorite of the research community when comes to experimentation. As a reference, PyTorch citations in papers on ArXiv grew 194 percent in the first half of 2019 alone, as noted by O'Reilly. For years, Facebook has based its deep learning work in a combination of PyTorch and Caffe2 and has put a lot of resources to support the PyTorch stack and developer community. Yesterday, Facebook released the latest version of PyTorch which showcases some state-of-the-art deep learning capabilities. There have been plenty of articles covering the launch of PyTorch 1.3.

Machine vision, or computer vision, is a popular research topic in artificial intelligence (AI) that has been around for many years. However, machine vision still remains as one of the biggest challenges in AI. In this article, we will explore the use of deep neural networks to address some of the fundamental challenges of computer vision. In particular, we will be looking at applications such as network compression, fine-grained image classification, captioning, texture synthesis, image search, and object tracking. Texture synthesis is used to generate a larger image containing the same texture.

Get unbeatable offers with up to 90% off on cloud servers and up to $300 rebate for all products! Click here to learn more. As a milestone in the natural language processing field, Bidirectional Encoder Representations from Transformers (BERT) did not appear out of nowhere. Rather, the development of this complex model followed a long line of development for deep learning and neural network models. In this article, written by Shi En, Feng Yin, and Tiao Can, from the dialog algorithm team at Ant Financial, we will look at the evolution of some of the major deep learning models-from the very simplest to the most complex-that we have come to know and use nowadays. That is, from a simple neural cell to one of the most complex model used today-the Bidirectional Encoder Representations from transformers (BERT) model-this article aims to discuss the ways in which deep learning in the area of natural language processing has evolved and developed as well as discuss the future direction of natural language processing based on the industry trends.

Harnessing the wisdom of the community, Visual Studio IntelliCode is revolutionizing developer productivity. We started with AI-assisted IntelliSense and are now expanding the application of artificial intelligence to significantly accelerate learning, radically improve development agility, and increase code quality by means of two exciting new capabilities: whole line completions and refactoring. Technology is evolving so fast that every developer is constantly learning, whether you're adopting a new programming language, API, or architecture (e.g. Amidst this rate of technological change, existing tools are no longer sufficient for achieving agility as development teams are trying to accelerate their time-to-market and increase code quality. As a result, development tools need to radically evolve to satisfy the productivity demands of modern teams.

Let us emphasize, though, that the receptive field size is not the only factor contributing to the improved performance mentioned above. Other factors play a very important role: network depth (i.e., number of layers) and width (i.e., number of filters per layer), residual connections, batch normalization, to name only a few. In other words, while we conjecture that a large receptive field is necessary, by no means it is sufficient. Additional experimentation is needed to confirm this hypothesis: for example, researchers may experimentally investigate how classification accuracy changes as kernel sizes and strides vary for different architectures. This may indicate if, at least for those architectures, a large receptive field is necessary.

The recent state-of-the-art deep learning methods have significantly improved brain tumor segmentation. However, fully supervised training requires a large amount of manually labeled masks, which is highly time-consuming and needs domain expertise. Weakly supervised learning with scribbles provides a good trade-off between model accuracy and the effort of manual labeling. However, for segmenting the hierarchical brain tumor structures, manually labeling scribbles for each substructure could still be demanding. In this paper, we use only two kinds of weak labels, i.e., scribbles on whole tumor and healthy brain tissue, and global labels for the presence of each substructure, to train a deep learning model to segment all the sub-regions. Specifically, we train two networks in two phases: first, we only use whole tumor scribbles to train a whole tumor (WT) segmentation network, which roughly recovers the WT mask of training data; then we cluster the WT region with the guide of global labels. The rough substructure segmentation from clustering is used as weak labels to train the second network. The dense CRF loss is used to refine the weakly supervised segmentation. We evaluate our approach on the BraTS2017 dataset and achieve competitive WT dice score as well as comparable scores on substructure segmentation compared to an upper bound when trained with fully annotated masks.

In this paper, we introduce the problem of knowledge graph contextualization - that is, given a specific context, the problem of extracting the most relevant sub-graph of a given knowledge graph. The context in the case of this paper is defined to be the textual entailment problem, and more specifically an instance of that problem where the entailment relationship between two sentences P and H has to be predicted automatically. This prediction takes the form of a classification task, and we seek to provide that task with the most relevant external knowledge while eliminating as much noise as possible. We base our methodology on finding the shortest paths in the cost-customized external knowledge graph that connect P and H, and build a series of methods - starting with manually curated search heuristics and culminating in automatically extracted heuristics - to find such paths and build the most relevant sub-graph. We evaluate our approaches by measuring the accuracy of the classification on the textual entailment problem, and show that modulating the external knowledge that is used has an impact on performance. 1 Introduction Knowledge Graphs (KGs) contain a very large amount of knowledge about the world and phenomena within it. Such knowledge can be very useful in natural language processing (NLP) tasks such as question answering, textual entailment etc. - tasks that can benefit from a large amount of specialized, domain-specific knowledge. However, recent approaches that have tried to use KGs as sources of external knowledge for the textual entailment problem (Wang et al. 2019) have found that bringing in external knowledge from KGs comes with a significant downside - namely noise that is brought in from the external knowledge. This noise mainly occurs due to the fact that KGs are very large graphs that often contain wrong, repeated, and incomplete information. Retrieving a sub-graph of a given KG that is relevant to a given problem instance is a nontrivial task, and continues to be a topic of much research study.