Deep latent variable models combine the approximation abilities of deep neural networks and the statistical foundations of generative models. The induced data distribution is an infinite mixture model whose density is extremely delicate to compute. Variational methods are consequently used for inference, following the seminal work of Rezende et al. (2014) and Kingma and Welling (2014). We study the well-posedness of the exact problem (maximum likelihood) these techniques approximatively solve. In particular, we show that most unconstrained models used for continuous data have an unbounded likelihood. This ill-posedness and the problems it causes are illustrated on real data. We also show how to insure the existence of maximum likelihood estimates, and draw useful connections with nonparametric mixture models. Furthermore, we describe an algorithm that allows to perform missing data imputation using the exact conditional likelihood of a deep latent variable model. On several real data sets, our algorithm consistently and significantly outperforms the usual imputation scheme used within deep latent variable models.

We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings instead of graphs. Our junction tree variational autoencoder generates molecular graphs in two phases, by first generating a tree-structured scaffold over chemical substructures, and then combining them into a molecule with a graph message passing network. This approach allows us to incrementally expand molecules while maintaining chemical validity at every step. We evaluate our model on multiple tasks ranging from molecular generation to optimization. Across these tasks, our model outperforms previous state-of-the-art baselines by a significant margin.

To extract and learn representations leading to generative mechanisms from data, especially without making arbitrary decisions and biased assumptions, is a central challenge in most areas of scientific research particularly in connection to current major limitations of influential topics and methods of machine and deep learning as they have often lost sight of the model component. Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative mechanisms. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our method and find that it can disentangle observations from discrete dynamic systems, random and complex networks. We think that these causal inference techniques can contribute as key pieces of information for estimations of probability distributions complementing other more statistical-oriented techniques that otherwise lack model inference capabilities.

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While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo. Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. They can start from a blank slate, and under the right conditions they achieve superhuman performance. Like a child incentivized by spankings and candy, these algorithms are penalized when they make the wrong decisions and rewarded when they make the right ones – this is reinforcement. Reinforcement algorithms that incorporate deep learning can beat world champions at the game of Go as well as human experts playing numerous Atari video games.

I am looking for a series of videos that I can use to learn Pytorch. I found out that there are some presentations, but any recommended videos to go through Deep Learning tutorials with Pytorch would be appreciated.

True to form, artificial intelligence continues to equal and even surpass doctors in the prediction and diagnosis of condition after condition. Most of this work, however, has occurred in carefully controlled laboratory experiments, with clean databases and images acquired and reviewed by experts. Now, companies are making a concerted push to bring AI into real healthcare settings, where things are messier and far less controlled. Last year, the U.S. Food and Drug Administration (FDA) approved the first machine learning application for healthcare: The Arterys Cardio DL. It uses a deep learning algorithm to analyze MRI images of the heart.

We not only help you keep operating costs down across the platform but also ensure that you can quickly begin to apply deep learning applications to work. Hardware resources need to be sufficient to synthesise large volumes of data to drive insightful actions. Fusion Informatics is one of the leading solutions providers for deep learning around the globe.

Time for an update on my One-Shot learning approach using a Siamese LSTM-based Deep Neural Network we developed for telecommunication network fault identification through traffic analysis. A lot of small details had to change as we upgraded our machine to the latest TensorFlow and Keras. That alone introduced a few new behaviors… As well as we obtained new data for new examples and found out some problems with our model. I don't intend to go through all changes, but some of the main ones as well as some interesting findings. It feels a lot like potty training a cat… If you are new to this series, you can refer to my previous posts: "Do Telecom Networks Dreams of Siamese Memories?" and "What Siamese Dreams are made of…" First, Batch Normalization in Keras is now on my black magic list .

In its recently released fourth-quarter and full-year earnings report, NVIDIA (NASDAQ: NVDA) showed the world that it could continue to put up blockbuster revenue and earnings numbers. The company grew revenue 34% over the prior-year quarter, while data center revenue based on AI (artificial intelligence) produced triple-digit year-over-year growth for the seventh consecutive quarter. It also beat analysts' estimates on both its top and bottom lines to produce a record-setting quarter. These were just some of the takeaways from the graphics-processing company's solid results. NVIDIA's earnings conference call also gave investors plenty of insight into the future, as the company's management talked cloud computing, deep learning, and self-driving cars.