Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show a straightforward extension of existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is undefined for set data. We propose a two fold modification to GAN algorithm for learning to generate point clouds (PC-GAN). First, we combine ideas from hierarchical Bayesian modeling and implicit generative models by learning a hierarchical and interpretable sampling process. A key component of our method is that we train a posterior inference network for the hidden variables. Second, instead of using only state-of-the-art Wasserstein GAN objective, we propose a sandwiching objective, which results in a tighter Wasserstein distance estimate than the commonly used dual form. Thereby, PC-GAN defines a generic framework that can incorporate many existing GAN algorithms. We validate our claims on ModelNet40 benchmark dataset. Using the distance between generated point clouds and true meshes as metric, we find that PC-GAN trained by the sandwiching objective achieves better results on test data than the existing methods. Moreover, as a byproduct, PC- GAN learns versatile latent representations of point clouds, which can achieve competitive performance with other unsupervised learning algorithms on object recognition task. Lastly, we also provide studies on generating unseen classes of objects and transforming image to point cloud, which demonstrates the compelling generalization capability and potentials of PC-GAN.

In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to predict the judgment results according to the given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for the contestants, i.e., predicting relevant law articles, charges and prison terms given the fact descriptions. CAIL2018 has attracted several hundreds participants (601 teams, 1, 144 contestants from 269 organizations). In this paper, we provide a detailed overview of the task definition, related works, outstanding methods and competition results in CAIL2018.

Fully convolutional neural networks (FCNs) have shown outstanding performance in many computer vision tasks including salient object detection. However, there still remains two issues needed to be addressed in deep learning based saliency detection. One is the lack of tremendous amount of annotated data to train a network. The other is the lack of robustness for extracting salient objects in images containing complex scenes. In this paper, we present a new architecture$ - $PDNet, a robust prior-model guided depth-enhanced network for RGB-D salient object detection. In contrast to existing works, in which RGB-D values of image pixels are fed directly to a network, the proposed architecture is composed of a master network for processing RGB values, and a sub-network making full use of depth cues and incorporate depth-based features into the master network. To overcome the limited size of the labeled RGB-D dataset for training, we employ a large conventional RGB dataset to pre-train the master network, which proves to contribute largely to the final accuracy. Extensive evaluations over five benchmark datasets demonstrate that our proposed method performs favorably against the state-of-the-art approaches.

In conjunction with today's release of PyTorch 1.0 Preview, we are broadening support for PyTorch throughout Google Cloud's AI platforms and services. PyTorch is a deep learning framework designed for easy and flexible experimentation. With the release of PyTorch 1.0 Preview, the framework now supports a fully hybrid Python and C/C front-end as well as fast, native distributed execution for production environments. Here at Google Cloud, we aim to support the full spectrum of machine learning (ML) practitioners, ranging from students and entrepreneurs who are just getting started to the world's top research and engineering teams. ML developers use many different tools, and we've integrated several of the most popular open source frameworks into our products and services, including TensorFlow, PyTorch, scikit-learn, and XGBoost.

The last few years have seen amazing progress in the field of artificial intelligence. AlphaGo beat the world's Go grandmasters, a feat thought impossible. The next version, AlphaZero, became the world's best chess player in four hours. Cars are driving themselves, smartphones can identify skin cancer as well as trained dermatologists, and we're on the verge of human-level universal translation. All of this is the result of not just machine learning, but actually a subset technology of machine learning called deep learning.

Researchers at the University of California, Berkeley have created a framework for teaching artificial intelligence systems to learn motion from being shown video clips on YouTube. The framework incorporates computer vision and reinforcement learning to train AI skills from videos. Altogether the team was able to train AI to perform more than 20 acrobatic tasks like cartwheels, handsprings, backflips, and some martial arts. The method does not require the use of motion capture video, the kind often used to transfer human action to digital forms, such as the movement of LeBron James incorporated into NBA 2K18 or the performance of Andy Serkis as Gollum from Lord of the Rings. The framework works by first ingesting the video to understand the poses seen in each video frame; then a simulated character is trained to imitate the movement using reinforcement learning.

Simply because artificial intelligence raises questions at all levels of society. Will robots take our work? Will AI destroy the human being and dominate the Earth? But also, how to overcome the current limits of Deep Learning and how to explain the decisions taken by the AI Systems, decisions from a so called "Black Box" that does not inspire much confidence … All these questions show the anxiety (logically legitimate: the human being and the living species in general are not appreciating so much the novelty and the uncertainties which result from it) concerning in general the advent of a new digital era, in which Artificial intelligence (and Big Data) is the engine. This article is intended to see things a little clearly in the questions posed above.

Bottom Line: An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative. Journal in Which the Study was Published: Clinical Cancer Research, a journal of the American Association for Cancer Research. Background: "In order to catch breast cancer early and help reduce mortality, mammography is an important screening exam; however, it currently suffers from a high false recall rate," said Wu. "These false recalls result in undue psychological stress for patients and a substantial increase in clinical workload and medical costs. Therefore, research on possible means to reduce false recalls in screening mammography is an important topic to investigate." How the Study Was Conducted: Wu and colleagues studied whether a technique in artificial intelligence called deep learning could be applied to analyze a large set of mammograms in order to distinguish images from women with a malignant diagnosis, images from women who were recalled and were later determined to have benign lesions (false recalls), and images from women determined to be breast cancer-free at the time of screening.

Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect.

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated. Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group unlabeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. What kind of problems does deep learning solve, and more importantly, can it solve yours?