Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning. The underlying premises are that there exist correlations among input parts and among output targets, and the predictive performance would increase when the correlations are incorporated. In this paper, we propose Column Bundle (CLB), a novel deep neural network for capturing the shared statistics in data. CLB is generic that the same architecture can be applied for various types of shared statistics by changing only input and output handling. CLB is capable of scaling to thousands of input parts and output labels by avoiding explicit modeling of pairwise relations. We evaluate CLB on different types of data: (a) multi-label, (b) multi-view, (c) multi-view/multi-label and (d) multi-instance. CLB demonstrates a comparable and competitive performance in all datasets against state-of-the-art methods designed specifically for each type.

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

Artificial intelligence is progressing rapidly. Advancements in deep learning have propelled the idea of an AI-powered world from a faraway fantasy to a fast-approaching reality. AI, whether it be in the form of personal assistants on our smartphones or data miners who predict our spending habits, is becoming more deeply integrated with our everyday lives. What's more, we're starting to see artificial intelligence make its way into larger applications. Perhaps the most exciting applications are seen in nascent AI healthcare technologies.

The Google-owned star British artificial intelligence company DeepMind is in talks with the National Grid about a potential partnership, with the possibility of using the technology to make the supply of energy across the UK more efficient. "There's huge potential for predictive machine learning technology to help energy systems reduce their environmental impact," said a spokesperson for the company. "One really interesting possibility is whether we could help the National Grid maximise the use of renewables through using machine learning to predict peaks in demand and supply." DeepMind's AI technology, which became famous after beating a human player at the chess-like game Go, has already been put to work for Google, reducing the energy needed for cooling its data centres by 40 per cent last year and increasing efficiency by 15 per cent. And co-founder Mustafa Suleyman outlined last year his hopes that this same technique could be applied to the National Grid and other large scale infrastructure. Read more: Here's how Google's DeepMind is using blockchain-like technology Now that has developed into early-stage talks taking place more recently between DeepMind – named City A.M's most innovative company of the year at the City A.M. Awards – and the National Grid, although there is no guarantee of anything being agreed.

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

The self-driving car space is getting increasingly more cutthroat. The sheer number of lawsuits filed recently are a testament to that. Tesla, for example, is suing its former Autopilot director Sterling Anderson. The lawsuit claims Anderson stole data for a competing venture, Aurora Innovations, that hasn't even come out of stealth mode yet. "In their zeal to play catch-up, traditional automakers have created a get-rich-quick environment. Small teams of programmers with little more than demoware have been bought for as much as a billion dollars. Cruise Automation, a 40-person firm, was purchased by General Motors in July 2016 for nearly $1 billion. In August 2016, Uber acquired Otto, another self-driving startup that had been founded only seven months earlier, in a deal worth more than $680 million."

This new form of Artificial General Intelligence runs over the top of Deep Learning and Machine Learning based systems to understand and act upon changing and unlabeled data in real-time. It's the inevitable conclusion for how all Artificial Intelligence will be built in the future. And PROME has a 6-year head start.

IBM researchers have set a milestone in conversational speech recognition by achieving a new industry record of a 5.5 percent word error rate, surpassing its previous record of 6.9 percent, according to the company's blog post. The researchers conducted a difficult speech recognition task to achieve this record, where they recorded conversations between humans discussing typical everyday topics like "buying a car." This recorded corpus, titled "SWITCHBOARD", has been used for over two decades to benchmark speech recognition systems. To achieve the 5.5 percent record, the researchers focused on extending the company's application of deep learning technologies by combining LSTM (Long Short Term Memory) and WaveNet language models with three strong acoustic models. The first two models were six-layer bidirectional LSTMs, with one of the models being equipped with multiple feature inputs and the other being trained with speaker-adversarial multi-task learning.

Among all of the self-driving startups working towards Level 4 autonomy (a self-driving system that doesn't require human intervention in most scenarios), Mountain View, Calif.-based Drive.ai's Drive sees deep learning as the only viable way to make a truly useful autonomous car in the near term, says Sameep Tandon, cofounder and CEO. "If you look at the long-term possibilities of these algorithms and how people are going to build [self-driving cars] in the future, having a learning system just makes the most sense. There's so much complication in driving, there are so many things that are nuanced and hard, that if you have to do this in ways that aren't learned, then you're never going to get these cars out there." It's only been about a year since Drive went public, but already, the company has a fleet of four vehicles navigating (mostly) autonomously around the San Francisco Bay Area--even in situations (such as darkness, rain, or hail) that are notoriously difficult for self-driving cars. Last month, we went out to California to take a ride in one of Drive's cars, and to find out how they're using deep learning to master autonomous driving.

The advent of easily accessible, mondo computing power could open up lots of possibilities in the arts, not just in the startup and tech world. Brooklyn startup Paperspace has a new post out about how to use its product (which is sort of like AWS for the computational power required of machine learning) to make art. The post centers on "style transfer," which is taking the style of one image and imparting it onto the content of another. "The central problem of style transfer revolves around our ability to come up with a clear way of computing the'content' of an image as distinct from computing the'style' of an image," the post reads. "Before deep learning arrived at the scene, researchers had been handcrafting methods to extract the content and texture of images, merge them and see if the results were interesting or garbage."