The field of artificial intelligence and machine learning, particularly the subcategory of deep learning, has experienced massive growth in recent years, with applications ranging from speech recognition to material inspection, healthcare to gaming, to name but a few. One area that's being transformed by machine learning is tomographic imaging – in which a series of data projections (such as X-ray radiographs, for example) are reconstructed into a three-dimensional image. A newly published book, Machine Learning for Tomographic Imaging, presents a detailed overview of the emerging discipline of deep-learning-based tomographic imaging. The book arose from discussions among four colleagues with a long-standing interest in advanced medical image reconstruction: Ge Wang from Rensselaer Polytechnic Institute, Yi Zhang of Sichuan University, Xiaojing Ye from Georgia State University and Xuanqin Mou from Xi'an Jiaotong University. "Deep tomographic reconstruction is a new area, and the development of this area has been rapid over the past years," explains Wang.

In the context of blockchain analysis, the bias-variance friction is present everywhere. Let's go back to our algorithm that attempts to predict price with a number of blockchain factors. If we use a simple linear regression method, the model is likely to underfit. However, if we use a super complex neural network with a small dataset, the model is likely to overfit. Using machine learning to analyze blockchain data is a very nascent space. As a result, most of the models are encountering the traditional challenges with machine learning applications. Overfitting is one of those omnipresent challenges in blockchain analysis fundamentally due to the lack of labeled data and trained models. There is no magic solution to fight overfitting but some of the principles outlined in this article have proven to be effective for us at IntoTheBlock.

Could natural language models improve their ability to answer questions on the fly? That's what a team of Amazon researchers set out to answer in a study scheduled to be presented at the 2020 Association for the Advancement of Artificial Intelligence in New York. They posit a method for adapting models based on Google's Transformer architecture -- which is particularly good at learning long-range dependencies among input data (such as the semantic and syntactic relationships between individual words of a sentence) -- to address the problem of answer selection. The team says that in tests on a benchmark data set, their proposed model demonstrated a 10% absolute improvement in mean average precision (which measures the quality of a sorted list of answers according to the correctness of the ranking) over the previous state-of-the-art answer selection model, achieving an error rate reduction of 50%. The approach -- Transfer and Adapt, or TANDA -- was first proposed late last year but has since been refined.

Not only is data coming in faster and at higher volumes, but it is also coming in messier. Such "non-Euclidean domains" can be imagined as complicated graphs comprised of data points with specified relationships or dependencies with other data points. Deep learning research is now working hard to figure out how to approach these data-as-spaghetti sources through the notion of GNNs, or graph neural networks. With so much happening in this emerging field recently, this survey paper took the top of the list as the most saved article in users' collections on arXiv.org,

Learn to use functions and apply Codes. This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path.

Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! Jianfeng Gao: Historically, there are two approaches to achieve the goal. One is to use large data. The idea is that if I can collect all the data in the world, then I believe the representation learned from this data is universal. Because I see all of them. The other approach is that, since the goal of this representation is to serve different applications, how about I train the model using application-specific objective functions across many, many different applications? Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! What gets you up in the morning? Jianfeng Gao: It's like all the world-class research teams, our goal, ultimate goal, is to advance the state-of-the-art and we want to push the AI frontiers by using deep learning technology or developing new deep learning technologies.

Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! Jianfeng Gao: Historically, there are two approaches to achieve the goal. One is to use large data. The idea is that if I can collect all the data in the world, then I believe the representation learned from this data is universal. Because I see all of them. The other approach is that, since the goal of this representation is to serve different applications, how about I train the model using application-specific objective functions across many, many different applications? Host: You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. Host: Dr. Jianfeng Gao is a veteran computer scientist, an IEEE Fellow and the current head of the Deep Learning Group at Microsoft Research. He and his team are exploring novel approaches to advancing the state-of-the-art on deep learning in areas like NLP, computer vision, multi-modal intelligence and conversational AI. Today, Dr. Gao gives us an overview of the deep learning landscape and talks about his latest work on Multi-task Deep Neural Networks, Unified Language Modeling and vision-language pre-training. He also unpacks the science behind task-oriented dialog systems as well as social chatbots like Microsoft Xiaoice, and gives us some great book recommendations along the way! What gets you up in the morning? Jianfeng Gao: It's like all the world-class research teams, our goal, ultimate goal, is to advance the state-of-the-art and we want to push the AI frontiers by using deep learning technology or developing new deep learning technologies.

Predictive Maintenance is an active field of research in every area. Especially in recent years, a great boom is registered in Machine Learning solutions. Some business invests a great amount of money to develop solutions which can predict in advance a possible fault occurring in the business activities. An interesting domain for these studies is the IoT industry, where we have sensors which monitor the working status of a machine or a particular engine part. The classical approach for this kind of problem involves usually the adoption of time series models in conjunction with signal process techniques which enable us to extract value from high-frequency data.

An innovative physics-guided learning algorithm for predicting the mechanical response of materials and structures is proposed in this paper. The key concept of the proposed study is based on the fact that physics models are governed by Partial Differential Equation (PDE), and its loading/ response mapping can be solved using Finite Element Analysis (FEA). Based on this, a special type of deep convolutional neural network (DCNN) is proposed that takes advantage of our prior knowledge in physics to build data-driven models whose architectures are of physics meaning. This type of network is named as FEA-Net and is used to solve the mechanical response under external loading. Thus, the identification of a mechanical system parameters and the computation of its responses are treated as the learning and inference of FEA-Net, respectively. Case studies on multi-physics (e.g., coupled mechanical-thermal analysis) and multi-phase problems (e.g., composite materials with random micro-structures) are used to demonstrate and verify the theoretical and computational advantages of the proposed method.

Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods. However when KP is applied to large Natural Language Processing tasks, it leads to significant accuracy loss (approx 26%). This paper proposes a way to recover accuracy otherwise lost when applying KP to large NLP tasks, by allowing additional degrees of freedom in the KP matrix. More formally, we propose doping, a process of adding an extremely sparse overlay matrix on top of the pre-defined KP structure. We call this compression method doped kronecker product compression. To train these models, we present a new solution to the phenomenon of co-matrix adaption (CMA), which uses a new regularization scheme called co matrix dropout regularization (CMR). We present experimental results that demonstrate compression of a large language model with LSTM layers of size 25 MB by 25x with 1.4% loss in perplexity score. At 25x compression, an equivalent pruned network leads to 7.9% loss in perplexity score, while HMD and LMF lead to 15% and 27% loss in perplexity score respectively.