In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.

This paper studies the cooperative training of two probabilistic models of signals such as images. Both models are parametrized by convolutional neural networks (ConvNets). The first network is a descriptor network, which is an exponential family model or an energy-based model, whose feature statistics or energy function are defined by a bottom-up ConvNet, which maps the observed signal to the feature statistics. The second network is a generator network, which is a non-linear version of factor analysis. It is defined by a top-down ConvNet, which maps the latent factors to the observed signal. The maximum likelihood training algorithms of both the descriptor net and the generator net are in the form of alternating back-propagation, and both algorithms involve Langevin sampling. We observe that the two training algorithms can cooperate with each other by jumpstarting each other's Langevin sampling, and they can be naturally and seamlessly interwoven into a CoopNets algorithm that can train both nets simultaneously.

Logistic Regression is one of the world's most popular model used to solve classification problems in machine learning. This model will arm you with super powers to solve problems like classifying "spam" or "non-spam" emails, detect malignant tumours, blood pressure and so many more! I have always believed, before learning anything new, you should have a purpose to learn it. I hope you are motivated enough to learn it now! Let us begin with binary classification problem, which means'y' our output can have only two values '0 or 1'.

A little over a year ago, TIME engaged Nintendo President Tatsumi Kimishima in a wide-ranging conversation about the company's fledgling mobile strategy, its struggles with the Wii U, the rise of its toys-to-life Amiibo figurines and a mystery-cloaked next-gen platform then known only as "NX." Three mobile apps and a sold-out "classic" version of its 1980s NES console later, with a $299 hybrid/TV games console dubbed Nintendo Switch due on March 3, TIME caught up with Nintendo's principal figure to talk Switch, mobile profitability, how he's liking the job so far and more. Here, following our recent chats with Nintendo EPD director Shinya Takahashi and Nintendo Switch general producer Yoshiaki Koizumi, is a lightly edited transcript of our conversation with Kimishima. Tatsumi Kimishima: Mr. Takahashi started out as a designer, and then as far as his career at Nintendo, he really worked with various development teams, where he worked as a coordinator for different environments. He was the guy they would bring in to pull all of these disparate things together. That was his main job while working with development teams. One thing that's a little bit different between [Donkey Kong and Mario creator] Mr. Miyamoto, say, and Mr. Takahashi, is that Mr. Miyamoto is of course known as the father of Mario, as well as for the characters and games he's helped develop. Mr. Takahashi, by contrast, is someone who really covers everything.

Ahmed Elgoni felt like he'd struck gold. The 24-year-old video game developer from South Africa had in November secured a ticket to the Game Developers Conference in San Francisco -- a cultural mecca for anyone who wants to make video games. A sponsor would cover the cost of his round-trip flight from Cape Town. Just two weeks ago, he received his visa to enter the U.S. Then President Trump signed an executive order banning refugees and travelers from seven countries. Elgoni grew up in South Africa, but he was born in Sudan -- one of the countries listed as part of the travel ban. As a dual citizen, he now doesn't know if he can attend GDC, which runs from Feb. 27 to March 3. "No one's sure of what's happening," he said.

Squid and their cephalopod brethren have been the inspiration for many a science fiction creature. Their slippery appendages, huge proportions, and inking abilities can be downright shudder-inducing. But you should probably be more concerned by the cephalopod's huge brain--which not only helps it solve tricky puzzles, but also lets it converse in its own sign language. But it's not that: Certain kinds of squid send messages by manipulating the color of their skin. "Their body patterning is fantastic, fabulous," says Chuan-Chin Chiao, a neuroscientist at National Tsing Hua University in Taiwan.

Organizations have a brief window to experiment and become familiar with the strategies and technologies needed to get ready for an AI-first world, according to a new report from Avanade, the leading digital and cloud services provider. The Avanade Technology Vision 2017, looking at emerging trends for the next three years, finds that we are on the cusp of a new decade of digital disruption powered by artificial intelligence and automation. It states that the emerging AI-first era will bring powerful opportunities and capabilities to organizations – similar to the PC revolution of the 1990s – but they must begin transforming now. The Avanade report highlights that the emerging AI-first era is already creating new ways for organizations to interact with, serve, and empower customers and employees. For example, by augmenting employees' capabilities using AI – including intelligent automation, Robotic Process Automation (RPA) and physical automation – organizations will enable workers to achieve far more, faster, with more intelligence-driven actions that deliver better results.

Researchers at Google and several universities recently made key progress in developing artificial intelligence (AI) that is itself able to write AI software, reports MIT Technology Review . In an experiment, AI researchers from Google Brain allowed software to develop the design for a machine learning system to recognize human speech. This software produced better results than software designs for machine learning that had previously been created by people, the researchers write in a scientific paper submitted to a conference. This paper has not yet been subject to peer review – so testing of these results by other researchers still remains to be done. Other research groups have also reported similar progress in recent months in the field of machine learning using artificial neural networks – the most promising technology in artificial intelligence in recent years.

This repository contains the lecture slides and course description for the Deep Natural Language Processing course offered in Hilary Term 2017 at the University of Oxford. This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence. The ambiguities and noise inherent in human communication render traditional symbolic AI techniques ineffective for representing and analysing language data. This is an applied course focussing on recent advances in analysing and generating speech and text using recurrent neural networks.

Episode Summary: Accenture is a leading global professional services company in the tech space, providing services to many of the Fortune 500 and their global equivalents. The company recently conducted a study, combined with expertise from economists and AI researchers, about the longer-term economic impact of artificial intelligence around the world. In this episode, I spoke with Chief Technology Officer Paul Daugherty, who has been with Accenture since 1986, and who was joined by Global Technology R&D Lead Marc Carrel-Billiard. We met up at a coffee shop after an AI Summit in San Francisco, and I asked Paul and Marc about what they had learned from this newly-published study and what they consider to be the significant impacts of *AI and automation on the future job market. Brief Recognition: Paul Daugherty is Accenture's CTO and leads the company's Technology Innovation & Ecosystem group.