The 33rd annual conference on Neural Information Processing Systems (NeurIPS) is going to be held at Vancouver Convention Center, Vancouver, Canada from December 8th to 14th, 2019. The primary focus of the Foundation is the presentation of a continuing series of professional meetings known as the Neural Information Processing Systems Conference, held over the years at various locations in the United States, Canada and Spain. NeurIPS received a record-breaking 6743 submissions this year, of which 1428 were accepted. A popular learning paradigm is hypergraph-based semi-supervised learning (SSL) where the goal is to assign labels to initially unlabeled vertices in a hypergraph. Motivated by the fact that a graph convolutional network (GCN) has been effective for graph-based SSL, the authors propose HyperGCN, a novel GCN for SSL on attributed hypergraphs.

Artificial Intelligence models and programmes mimic the functioning of human brains, helping machines learn make decisions in a more-like manner. But how exactly they arrive at decisions is unknown. In order to understand this, a group of researchers at the Indian Institute of Technology (IIT-Hyderabad) have developed a method by which the inner workings of Artificial Intelligence models can be understood in terms of causal attributes. This finding assumes significance in the wake of emergence of regulations such as General Data Protection Regulation (GDPR) that requires organisations to explain the decisions made by machine learning methods. Modern Artificial Neural Networks, also called Deep Learning (DL), have increased tremendously in complexity such that machines can train themselves to process and learn from data that has been fed to them.

TensorFlow is one of the most popular and primary software tools for creating deep learning models. Mainly used for classification, prediction, perception, etc., there are numerous reasons why one should use this platform. Adding another feather to its diverse usage, Google this week introduced Neural Structured Learning (NSL), an open-source framework to learn neural networks. This framework works with the TensorFlow platform and is suitable for both beginners and advanced developers to use it for training neural networks with structured signals. The NSL can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.

Using deep learning and artificial intelligence (AI), FAU scientists are bringing to life one of about a handful of these quadruped robots in the world. Astro is unique because he is the only one of these robots with a head, 3D printed to resemble a Doberman pinscher, that contains a (computerized) brain. What would you get if you combined Apple's Siri and Amazon's Alexa with Boston Dynamic's quadruped robots? You'd get "Astro," the four-legged seeing and hearing intelligent robodog. Using deep learning and artificial intelligence (AI), scientists from Florida Atlantic University's Machine Perception and Cognitive Robotics Laboratory (MPCR) in the Center for Complex Systems and Brain Sciences in FAU's Charles E. Schmidt College of Science are bringing to life one of about a handful of these quadruped robots in the world.

Google's DeepMind AI division will likely end up making the next generation of military killbots, but before then, at least they'll provide new challenges for the esports crowd. To make sure it wasn't a fluke, they've unleashed AlphaStar on the European public. According to this official blog post, AlphaStar is limited to Europe for now. StarCraft II players can opt for a chance to have their next 1v1 partner partner swapped out for an unfeeling machine that's less likely to insult your mother. The good news is that AlphaStar isn't going to be learning bad habits and worse language from StarCraft 2's player population.

This week the Allen Institute for Artificial Intelligence announced a breakthrough for a BERT-based model, passing an eighth-grade science test. The GPU-accelerated system called Aristo can read, learn, and reason about science, in this case emulating the decision making of students. For this milestone, Aristo answered more than 90 percent of the questions on an eighth-grade science exam correctly, and 83 percent on a 12th-grade exam. "Although Aristo only answers multiple choice questions without diagrams, and operates only in the domain of science, it nevertheless represents an important milestone towards systems that can read and understand," the researchers stated in a newly published paper on ArXiv. "The momentum on this task has been remarkable, with accuracy moving from roughly 60% to over 90% in just three years," Though no diagrams were used for this particular task, the work as a whole integrates multiple AI-based technologies including natural language processing, information extraction, knowledge representation and reasoning, commonsense knowledge, and diagram understanding.

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage. Images are captured by an 8-megapixel camera sensor capable of recording in 4K UHD. The device also comes with a four-microphone array and speaker that can be utilized for building voice-based user interfaces.

What are the biggest misconceptions people have about machine learning, deep learning, and AI? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. This is a great question, particularly in the cybersecurity industry where these terms are used interchangeably. Artificial intelligence is the broad concept of using non-biological intelligence to process data, interpret it, and derive learnings to inform decision making. Machine learning and deep learning are a subset of AI. Both machine learning and deep learning use mathematical models from data to help inform decision making.

The characterization of amorphous materials is especially challenging because their lack of long-range order makes it difficult to define structural metrics. In this work, we apply deep learning algorithms to accurately classify amorphous materials and characterize their structural features. Specifically, we show that convolutional neural networks and message passing neural networks can classify two-dimensional liquids and liquid-cooled glasses from molecular dynamics simulations with greater than 0.98 AUC, with no a priori assumptions about local particle relationships, even when the liquids and glasses are prepared at the same inherent structure energy. Furthermore, we demonstrate that message passing neural networks surpass convolutional neural networks in this context in both accuracy and interpretability. We extract a clear interpretation of how message passing neural networks evaluate liquid and glass structures by using a self-attention mechanism. Using this interpretation, we derive three novel structural metrics that accurately characterize glass formation. The methods presented here provide us with a procedure to identify important structural features in materials that could be missed by standard techniques and give us a unique insight into how these neural networks process data. I. INTRODUCTION Classifying material structures and predicting their properties are important tasks in materials science. The behavior of materials often depends strongly on their underlying structure, and understanding these structure-property relationships relies on accurately describing the structural features of a material. However, quantifying structure-property relationships and identifying structural features in complex materials are difficult tasks. A variety of standard techniques have been developed to analyze material structures. Some of the most common techniques include the Steinhardt bond order parameters, 1 Bond Angle Analysis (BAA), 2 and Common Neighbor Analysis (CNA), 3 which are useful for detecting order-disorder transitions and differentiating between crystal structures in ordered samples. As discussed in Reinhardt et al., 4 the Steinhardt bond order parameters can be stymied by thermal fluctuations or am-a) Electronic mail: swansonk1@uchicago.edu BAA relies on a small set of crystalline reference structures that may not be present in amorphous samples. CNA is more flexible than BAA, but it cannot provide accurate information about particles that do not exhibit known symmetries, making analysis of irregular structures challenging.

Recurrent neural networks have been widely used to generate millions of de novo molecules in a known chemical space. These deep generative models are typically setup with LSTM or GRU units and trained with canonical SMILEs. In this study, we introduce a new robust architecture, Generative Examination Networks GEN, based on bidirectional RNNs with concatenated sub-models to learn and generate molecular SMILES with a trained target space. GENs autonomously learn the target space in a few epochs while being subjected to an independent online examination mechanism to measure the quality of the generated set. Here we have used online statistical quality control (SQC) on the percentage of valid molecules SMILES as an examination measure to select the earliest available stable model weights. Very high levels of valid SMILES (95-98%) can be generated using multiple parallel encoding layers in combination with SMILES augmentation using unrestricted SMILES randomization. Our architecture combines an excellent novelty rate (85-90%) while generating SMILES with a strong conservation of the property space (95-99%). Our flexible examination mechanism is open to other quality criteria.