Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been shown to have the advantages of being computationally more efficient in inference than LSTM LMs with close performance and being able to flexibly integrating rich features. In this paper we propose neural TRFs, beyond of the previous discrete TRFs that only use linear potentials with discrete features. The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework. Neural TRFs combine the advantages of both NNs and TRFs. The benefits of word embedding, nonlinear feature learning and larger context modeling are inherited from the use of NNs. At the same time, the strength of efficient inference by avoiding expensive softmax is preserved. A number of technical contributions, including employing deep convolutional neural networks (CNNs) to define the potentials and incorporating the joint stochastic approximation (JSA) strategy in the training algorithm, are developed in this work, which enable us to successfully train neural TRF LMs. Various LMs are evaluated in terms of speech recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The results show that neural TRF LMs not only improve over discrete TRF LMs, but also perform slightly better than LSTM LMs with only one fifth of parameters and 16x faster inference efficiency.

We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E and PDBBind databases.

Deep Learning has become one of the primary research areas in developing intelligent machines. Most of the well-known applications (such as Speech Recognition, Image Processing and NLP) of AI are driven by Deep Learning. Deep Learning algorithms mimic human brains using artificial neural networks and progressively learn to accurately solve a given problem. But there are significant challenges in Deep Learning systems which we have to look out for. If you look around, you might realize the power of the above statement by Andrew.

German Chancellor Angela Merkel is introduced to Pepper the Robot by DigitalSTROM employee Martin Vesper in Berlin, Germany. Deep learning, a popular form of machine learning, is being applied across across a number of the latest tech products and services. But for the most part, all that computing is still taking place in the cloud. San Francisco-based artificial intelligence startup Skymind is hoping to embed deep learning directly into robots. The startup is releasing a new toolkit, SKIL Somatic, that will enable robots to recognize the visual world around it using a popular type of deep learning, called a convolutional neural networks.

I have never used a deep learning framework, like TensorFlow or Keras. I have never touched a GPU. I don't have a degree in computer science or statistics. My degree is in mechanical engineering, of all things. But I haven't given up hope.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence -- we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

Deep learning is a powerful tool that identifies patterns, extracts meaning from large, diverse datasets, and solves complex problems. However, integrating neural networks into existing compute environments is a challenge that often requires specialized and costly infrastructure. New software and hardware options will simplify the complexity. Intel Omni-Path Architecture (Intel OPA) is well suited to the demands of deep learning, enabling near-linear scalability across large numbers of nodes to provide fast time to results for large problems (see Figure 1). A key focus of deep learning implementations is to reduce the time to train the model and to ensure a high level of accuracy.

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

One of the problems we're most interested in and have worked on a bunch is object detection. Like many other computer vision problems, there still isn't an obvious or even "best" way to approach the problem, meaning there's still much room for improvement. Before getting into object detection, let's do a quick rundown of the most common problems in the field. It consists of classifying an image into one of many different categories. One of the most popular datasets used in academia is ImageNet, composed of millions of classified images, (partially) utilized in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) annual competition.