We study the problem of predicting a set from a feature vector with a deep neural network. Existing approaches ignore the set structure of the problem and suffer from discontinuity issues as a result. We propose a general model for predicting sets that properly respects the structure of sets and avoids this problem. With a single feature vector as input, we show that our model is able to auto-encode point sets, predict bounding boxes of the set of objects in an image, and predict the attributes of these objects in an image.

Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.

We propose a simple auto-encoder framework for molecule generation. The molecular graph is first encoded into a continuous latent representation $z$, which is then decoded back to a molecule. The encoding process is easy, but the decoding process remains challenging. In this work, we introduce a simple two-step decoding process. In a first step, a fully connected neural network uses the latent vector $z$ to produce a molecular formula, for example CO$_2$ (one carbon and two oxygen atoms). In a second step, a graph convolutional neural network uses the same latent vector $z$ to place bonds between the atoms that were produced in the first step (for example a double bond will be placed between the carbon and each of the oxygens). This two-step process, in which a bag of atoms is first generated, and then assembled, provides a simple framework that allows us to develop an efficient molecule auto-encoder. Numerical experiments on basic tasks such as novelty, uniqueness, validity and optimized chemical property for the 250k ZINC molecules demonstrate the performances of the proposed system. Particularly, we achieve the highest reconstruction rate of 90.5\%, improving the previous rate of 76.7\%. We also report the best property improvement results when optimization is constrained by the molecular distance between the original and generated molecules.

While model-based deep reinforcement learning (RL) holds great promise for sample efficiency and generalization, learning an accurate dynamics model is often challenging and requires substantial interaction with the environment. A wide variety of domains have dynamics that share common foundations like the laws of classical mechanics, which are rarely exploited by existing algorithms. In fact, humans continuously acquire and use such dynamics priors to easily adapt to operating in new environments. In this work, we propose an approach to learn task-agnostic dynamics priors from videos and incorporate them into an RL agent. Our method involves pre-training a frame predictor on task-agnostic physics videos to initialize dynamics models (and fine-tune them) for unseen target environments. Our frame prediction architecture, SpatialNet, is designed specifically to capture localized physical phenomena and interactions. Our approach allows for both faster policy learning and convergence to better policies, outperforming competitive approaches on several different environments. We also demonstrate that incorporating this prior allows for more effective transfer between environments.

Graph Neural Network has become the new fashion in many graph-based learning problems. Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (e.g. As the team behind this library, we want to share with you the new release of DGL (v0.3) that is much faster (up to 19x faster) and more scalable for training GNNs on large graphs (up to 8x larger). For whom have never heard of DGL or Graph Neural Network, maybe it is worth to take a look at this new trend of geometric deep learning. Checkout this 10-minute tutorial about how to use Graph Neural Network to predict community membership (https://docs.dgl.ai/tutorials/basics/1_first.html).

Using deep learning for image recognition allows a computer to learn from a training data set what the important "features" of the images are. By using a hierarchy of numerous artificial neurons, deep learning can automatically classify images with a high degree of accuracy. Thus, neural networks can recognize different species of cats, or models of cars or airplanes from images. Sometimes neural networks can exceed the performance of the human eye for certain applications.

In the 20 years I've been traveling to IBC, I've tried to seek out new technology, work practices and trends that could benefit my clients and help them be more competitive. One thing that is perennially exciting about this industry is the rapid pace of change. Certainly, from a post production point of view, there is a mini revolution every three years or so. In the past, those revolutions have increased image quality or the efficiency of making those images. The current revolution is to leverage the power and flexibly of cloud computing.

Machines use Google-type algorithms on biopsy images to help children get treatment faster. A study published in the open access journal JAMA Open Network today by scientists at the University of Virginia schools of Engineering and Medicine says machine learning algorithms applied to biopsy images can shorten the time for diagnosing and treating a gut disease that often causes permanent physical and cognitive damage in children from impoverished areas. In places where sanitation, potable water and food are scarce, there are high rates of children suffering from environmental enteric dysfunction, a disease that limits the gut's ability to absorb essential nutrients and can lead to stunted growth, impaired brain development and even death. The disease affects 20 percent of children under the age of 5 in low- and middle-income countries, such as Bangladesh, Zambia and Pakistan, but it also affects some children in rural Virginia. For Dr. Sana Syed, an assistant professor of pediatrics in the UVA School of Medicine, this project is an example of why she got into medicine.

With FMX 2019 just finishing up May 3 in Stuttgart, Germany, we're pleased to present our latest set of exclusive interviews with some of the talented speakers who presented over the packed four-day conference, a group of top industry professionals who converge on the event to lecture, teach and network. Watch and enjoy our insightful and entertaining AWN @ FMX 2019 Professional Spotlight video series featuring some of the biggest names in animation, visual effects, computer graphics and transmedia. Stay tuned as we add dozens of brand new interviews over the coming weeks. You can also check out last year's AWN - FMX 2018 interviews, our set from FMX 2017 as well as the complete collection of AWN - FMX Professional Spotlight videos. You can also check out AWN's great FMX 2019 coverage on our FMX Conference Spotlight blog.

Architecture and weights are two essential considerations for artificial neural networks. Architecture is akin to the innate human brain, and contains the neural network's initial settings such as hyperparameters, layers, node connections (or wiring), etc. Weights meanwhile are the relative strength of the different connections between nodes after model training, which can be likened to a human brain that has learned for example how to multiply numbers or speak French. As with the age-old "nature versus nurture" debate, AI researchers want to know whether architecture or weights play the main role in the performance of neural networks. In a blow to the "nurture" side, Google researchers have now demonstrated that a neural network which has not learned weights through training can still achieve satisfactory results in machine learning tasks. Google Brain researchers Adam Gaier and David Ha said their idea was inspired by precocial behaviors that have evolved in nature, explaining in a blog post: "In biology, precocial species are those whose young already possess certain abilities from the moment of birth. There is evidence to show that lizard and snake hatchlings already possess behaviors to escape from predators. Shortly after hatching, ducks are able to swim and eat on their own, and turkeys can visually recognize predators."