I don't see why you're so eager to bash this that hard. Most GAN papers work on images 128x128 which is about the sample size in 1s audio, and even with the most clever tricks so far like LAPGAN or PGGAN the best is about 1024x1024 images. This is the very first published GAN model that is successfully trained with 1-D convolutions without skip connections - which means that it can generate audio samples with completely unsupervised fashion directly from latent samples. Can you imagine the new possibilities on generative audio modeling stemming from this, like people did on images during last couple years? Also, people created videos from frames obtained from CycleGAN and they didn't linearly scale everything like you like to do so much.

Well, some people try to apply algebraic topology (and even algebraic geometry) to ML, so abstract algebra, a prerequisite for AT and AG, is useful in that sense. However, I'd rather read many good recent papers in deep learning to apply them for your research instead of studying AA and AT, as I see that's likely to result in more substantial results. Some recent AT application to ML includes On Characterizing the Capacity of Neural Networks using Algebraic Topology .

As wise speakers go, Apple's HomePod is late to the birthday party. The company has made a addiction out of no longer letting the competition pressure it into dashing to market with an unfinished product. The iPod, iPhone, and Apple Watch are just just a few examples of Apple's tried-and-true approach of now not being first. With the $ 350 HomePod, Apple positions it as a superior speaker first, with the delivered bonus of Siri. And, from that point of view, HomePod is a clear winner.

This has been a big week for specialized chips aimed at performing machine learning tasks. Google announced the open beta of its second generation Tensor Processing Unit, Amazon is reportedly working on a dedicated AI chip for its Echo smart speaker, and ARM announced its own AI hardware.

As 4,000 VIP guests gather at the World Government Summit in Dubai to discuss everything from business, politics and technology, Inspire Middle East brings to you exclusive interviews with two of the most illuminating personalities on the ground. World-famous producer and musician, will.i.am is also a passionate tech entrepreneur. After his company built an Artificial Intelligence operating system, he was invited to the summit by the UAE's Minister of Artificial Intelligence, His Excellency Omar bin Sultan Al Olama. Will.i.am's activities transcends music, business and technology, going all the way to education. "I also have a school – in the ghetto that I'm from – where we teach kids computer science and engineering – starting from nine years of age, up to 18. "I was one of those kids who was bust out from the ghetto to learn computer science at an early age – and now that I've had success in music I want to go back to my neighbourhood, so kids don't have to leave.

I was reading this interesting paper on importance weighted active learning: http://cseweb.ucsd.edu/ I'm trying to implement a basic example of active learning using sklearn. I'm looking at how to implement the "implicit" version. However, I don't understand the meaning of "If learn(S (xt, 1)) and learn(S (xt, 0)) both return an answer". Is there anyone who can shed some light or point me to resources that'll help me implement importance weighted active learning?

This book gives clear guidance on how to implement statistical and machine learning methods for newcomers to this field. It's filled with practical real-world examples of where and how algorithms work. For those with an inclination towards R programming, this book even has practical examples in R. In case you're not a programmer, don't let that put you off.

Machine Learning has gradually spread into our lives in different ways. For example, your newly recommended music from Spotify and videos from Netflix. People are talking about machine learning all the time and you might have already hear about it several times in TV shows, news, or even in animations. However you might still be confused about what it really is? Here is a simple quick intro to Machine Learning for non-tech people.

The first event will feature Yann LeCun, Facebook's director of AI research and a member of the New York University faculty. LeCun will inaugurate the series on Tuesday, February 20, 2018, from 10 to 11 am in Downtown Brooklyn, at NYU Tandon's Pfizer Auditorium, 5 MetroTech Center. Registration is free and open to all. His address, "Obstacles to Deep Learning and AI," will explore a new frontier: predictive models that capture the "common sense" exhibited by humans and animals, who often learn by observation and occasional action. LeCun's pioneering work in the application of neural networks to computer vision and other AI areas led to products and services deployed across most technology companies.