Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative process. Experimental results show that PNAT achieves top results on machine translation and paraphrase generation tasks, outperforming several strong baselines.

The ability to predict city-wide parking availability is crucial for the successful development of Parking Guidance and Information (PGI) systems. Indeed, the effective prediction of city-wide parking availability can improve parking efficiency, help urban planning, and ultimately alleviate city congestion. However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e.g., camera, ultrasonic sensor, and GPS). To this end, we propose Semi-supervised Hierarchical Recurrent Graph Neural Network (SHARE) for predicting city-wide parking availability. Specifically, we first propose a hierarchical graph convolution structure to model non-Euclidean spatial autocorrelation among parking lots. Along this line, a contextual graph convolution block and a soft clustering graph convolution block are respectively proposed to capture local and global spatial dependencies between parking lots. Additionally, we adopt a recurrent neural network to incorporate dynamic temporal dependencies of parking lots. Moreover, we propose a parking availability approximation module to estimate missing real-time parking availabilities from both spatial and temporal domain. Finally, experiments on two real-world datasets demonstrate the prediction performance of SHARE outperforms seven state-of-the-art baselines.

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

There has been considerable debate over 2D and 3D representation learning on 3D medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they are generally weak in capturing large 3D contexts. 3D approaches are natively strong in 3D contexts, however few publicly available 3D medical dataset is large and diverse enough for universal 3D pretraining. Even for hybrid (2D + 3D) approaches, the intrinsic disadvantages within the 2D / 3D parts still exist. In this study, we bridge the gap between 2D and 3D convolutions by reinventing the 2D convolutions. We propose ACS (axial-coronal-sagittal) convolutions to perform natively 3D representation learning, while utilizing the pretrained weights from 2D counterparts. In ACS convolutions, 2D convolution kernels are split by channel into three parts, and convoluted separately on the three views (axial, coronal and sagittal) of 3D representations. Theoretically, ANY 2D CNN (ResNet, DenseNet, or DeepLab) is able to be converted into a 3D ACS CNN, with pretrained weights of same parameter sizes. Extensive experiments on proof-of-concept dataset and several medical benchmarks validate the consistent superiority of the pretrained ACS CNNs, over the 2D / 3D CNN counterparts with / without pretraining. Even without pretraining, the ACS convolution can be used as a plug-and-play replacement of standard 3D convolution, with smaller model size.

Deep learning, the branch of AI that uses artificial neural networks to build prediction and pattern matching models from large datasets relevant to a particular application, is having a sizable impact on both consumer and enterprise software. Whether for enabling home appliances to understand and respond to vocal commands or identifying hidden patterns endemic to all malware, deep learning algorithms allow machines to mimic and even improve upon human cognition in ways that are impossible with imperative or declarative programming. Unfortunately, developing deep learning software isn't easy since the models are customized for a particular use. Indeed, developing models is more like making a custom-fitted suit, not off-the-rack clothing in standard sizes. Deep learning encompasses a large category of software, not a general-purpose solution, and describes a broad range of algorithms and network types, each better suited to particular types of problems and data than others.

We developed a convolutional deep neural network-based approach named DOVE (DOcking decoy selection with Voxel-based deep neural nEtwork) for evaluating protein docking models. To evaluate a protein docking model, DOVE scans the protein-protein interface of the model with a 3D voxel and considers atomic interaction types and their energetic contributions as input features applied to the neural network. The deep learning models were trained and validated on docking models available in the ZDock and DockGround databases. Among the different combinations of features tested, almost all outperformed existing scoring functions.

A successful new AI chip will have to meet several criteria, says Stevens. At a minimum, it has to be 10 or 100 times faster than the general-purpose processors when working with the lab's AI models. Many of the specialized chips are optimized for commercial deep-learning applications, like computer vision and language, but may not perform as well when handling the kinds of data common in scientific research. "We have a lot of higher-dimensional data sets," Stevens says--sets that weave together massive disparate data sources and are far more complex to process than a two-dimensional photo.

Strong Understanding of TensorFlow 2.0 from Vary Beginning Artificial Neural Networks (ANNs) in Tensorflow 2.0 Convolutional Neural Networks (CNNs) in Tensorflow 2.0 Deep Neural Networks (DNNs) in TensorFlow 2.0 Deep Learning Algorithms from Scratch in Python Using NumPy, Pandas, Matplotlib and TensorFlow 2.0 Activation Function, Cost Function, Gradient Descent and Backpropagation Over-fitting, Under-fitting, Training, Validation, Testing, and Initialization Google has recently released TensorFlow 2.0, it has so many features that simplify the model development, maintenance, processes and performanceNow it is very easy to build and deploy AI models in practice! Deep Learning is one of the fastest growing field of Artificial Intelligence, Deep Learning models can solve very hard and complex tasks... Strong Understanding of TensorFlow 2.0 from Vary Beginning Artificial Neural Networks (ANNs) in Tensorflow 2.0 Convolutional Neural Networks (CNNs) in Tensorflow 2.0 Deep Neural Networks (DNNs) in TensorFlow 2.0 Deep Learning Algorithms from Scratch in Python Using NumPy, Pandas, Matplotlib and TensorFlow 2.0 Activation Function, Cost Function, Gradient Descent and Backpropagation Over-fitting, Under-fitting, Training, Validation, Testing, and Initialization Strong Understanding of TensorFlow 2.0 from Vary Beginning

Machine Learning is something not many people will fully understand. It's vague because the human mind can't really determine how a computer "reads" and "performs" but simply put, machine learning is the study of statistical models and algorithms that a machine uses. A machine refers to a mobile device or computer. For this purpose, we're referring to smartphones as we look into how it helps Google Play Store users to see and discover new apps that may be relevant to them. DeepMind has been helping Google when it comes to AI (artificial intelligence).