This screencasts describes how to import a Neural Network that was created and trained using Keras, into DeepLearning4J Deeplearning4j - Skymind uploaded a video 2 weeks ago Skymind Academy - Duration: 91 seconds. Skymind Academy enables your team to build deep learning solutions. We offer private corporate seminars and public workshops. Deeplearning4j - Skymind uploaded a video 3 weeks ago What is Deep Learning? We explain what deep learning is and why it matters.

There is a lot of hype (as you know) related to Artificial Intelligence (AI), machine learning and specifically deep learning (complex neural networks). You also know (if you have been keeping up with the news) that we are all users of such techniques in many every day tools. But recently the technology has gotten a little too close for comfort. Some vendors in the data space, specifically focused on data quality, MDM and data management have started talking about how deep learning will change the use of those tools significantly. At this point, I am not so sure.

All eyes are on Montreal these days as a hub for deep learning. "Clearly it's a place where everybody wants to be if we want to tap into that talent," says Nagraj Kashyap, corporate vice-president of Microsoft Ventures in San Francisco. Montreal's pre-eminence as a deep learning centre can largely be attributed to the efforts of Yoshua Bengio, considered to be one of the three "co-fathers" of deep learning technology. Bengio not only engaged in cutting-edge research at the Université de Montréal long before deep learning was considered viable; his work has spawned an ecosystem that many say is unrivalled in the artificial intelligence (AI) world. That ecosystem includes the Montreal Institute for Learning Algorithms (MILA) which has been funded by government and private sector parties, including Google and Microsoft, among other tech notables.

Francesco is Data Scientist at Janssen Pharmaceutical Companies of Johnson & Johnson and a Science writer. He is committed to "A World Without Disease" paradigm shift in healthcare, leveraging Artificial Intelligence and Data Science to predict risk and intercepting diseases. He is focused on putting machine learning at the service of human beings. Do you know why you can't hear the ugly ahem sounds on the podcast Data Science at Home? Let me introduce the ahem detector, a deep convolutional neural network that is trained on transformed audio signals to recognize "ahem" sounds. The network has been trained to detect such signals on the episodes of Data Science at Home, the podcast about data science at worldofpiggy.com/podcast.

Beijing has given the green light for the creation of China's very first'national laboratory for deep learning', in a move that could help the country to surpass the United States in developing artificial intelligence (AI). The National Development and Reform Commission (NDRC) recently approved the plan to set up a national engineering'lab' for researching and implementing deep learning technologies. The lab will not have a physical presence, instead taking the form of a research network predominantly based online. Regarded as one of the most exciting and fastest-growing areas of AI, deep learning - a subdivision of machine learning - involves feeding data through virtual neural networks designed to mimic the human brain's decision-making process, in order to solve problems and recognise images and sounds. It is seen by many as the key to elevating AI to something approximating human intelligence, and is already credited with major breakthroughs in technologies such as voice recognition in smartphones.

We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. Deep generative models have recently become popular due to their ability to model input data distributions and generate realistic examples from those distributions. We present three classes of attacks on the VAE and VAE-GAN architectures and demonstrate them against networks trained on MNIST, SVHN and CelebA. Our first attack leverages classification-based adversaries by attaching a classifier to the trained encoder of the target generative model, which can then be used to indirectly manipulate the latent representation. Our second attack directly uses the VAE loss function to generate a target reconstruction image from the adversarial example. Our third attack moves beyond relying on classification or the standard loss for the gradient and directly optimizes against differences in source and target latent representations. We also motivate why an attacker might be interested in deploying such techniques against a target generative network.

When analyzing the genome, researchers have discovered that proteins bind to DNA based on certain patterns of the DNA sequence known as "motifs". However, it is difficult to manually construct motifs due to their complexity. Recently, externally learned memory models have proven to be effective methods for reasoning over inputs and supporting sets. In this work, we present memory matching networks (MMN) for classifying DNA sequences as protein binding sites. Our model learns a memory bank of encoded motifs, which are dynamic memory modules, and then matches a new test sequence to each of the motifs to classify the sequence as a binding or nonbinding site.

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied. However, our understanding of how the asymptotic convergence of backpropagation in deep architectures is related to the structural properties of the network and other design choices (like denoising and dropout rate) is less clear at this time. An interesting question one may ask is whether the network architecture and input data statistics may guide the choices of learning parameters and vice versa. In this work, we explore the association between such structural, distributional and learnability aspects vis-\`a-vis their interaction with parameter convergence rates. We present a framework to address these questions based on convergence of backpropagation for general nonconvex objectives using first-order information. This analysis suggests an interesting relationship between feature denoising and dropout. Building upon these results, we obtain a setup that provides systematic guidance regarding the choice of learning parameters and network sizes that achieve a certain level of convergence (in the optimization sense) often mediated by statistical attributes of the inputs. Our results are supported by a set of experimental evaluations as well as independent empirical observations reported by other groups.

Abstract--The current paper proposes a novel neural network model for recognizing visually perceived human actions. The proposed multiple spatiotemporal scales recurrent neural network (MSTRNN) model is derived by introducing multiple timescale recurrent dynamics to the conventional convolutional neural network model. One of the essential characteristics of the MSTRNN is that its architecture imposes both spatial and temporal constraints simultaneously on the neural activity which vary in multiple scales among different layers. As suggested by the principle of the upward and downward causation, it is assumed that the network can develop meaningful structures such as functional hierarchy by taking advantage of such constraints during the course of learning. T o evaluate the characteristics of the model, the current study uses three types of human action video dataset consisting of different types of primitive actions and different levels of compositionality on them. The performance of the MSTRNN in testing with these dataset is compared with the ones by other representative deep learning models used in the field. The analysis of the internal representation obtained through the learning with the dataset clarifies what sorts of functional hierarchy can be developed by extracting the essential compositionality underlying the dataset. ECENTL Y, a convolutional neural network (CNN) [1], inspired by a mammalian visual cortex, showed a remarkably better object image recognition performance than conventional vision recognition schemes which employ elaborately hand-coded visual features. A CNN trained with 1 million visual images from ImageNet [2] was able to classify hundreds of object images with an error rate of 6.67% [3], and demonstrated near-human performance [4]. As a consequence, CNNs are less effective in handling video image patterns than static images. To address this shortcoming, a number of action recognition models have been developed. H. Lee is with the Department of Electrical Engineering, Korea Institute of Science and Technology, Daejeon 305-701, Republic of Korea, email: (haanvidlee@gmail.com). M. Jung is with the Department of Electrical Engineering, Korea Institute of Science and Technology, Daejeon 305-701, Republic of Korea, email: (minju5436@gmail.com).