Abstract: In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 17.5 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, it has even fewer attention errors than the autoregressive model on the challenging test sentences. Furthermore, we build the first fully parallel neural text-to- speech system by applying the inverse autoregressive flow (IAF) as the parallel neural vocoder. Our system can synthesize speech from text through a single feed-forward pass.

Abstract: Gaussian processes (GPs) are flexible models with state-of-the-art performance on many impactful applications. However, computational constraints with standard inference procedures have limited exact GPs to problems with fewer than about ten thousand training points, necessitating approximations for larger datasets. In this paper, we develop a scalable approach for exact GPs that leverages multi-GPU parallelization and methods like linear conjugate gradients, accessing the kernel matrix only through matrix multiplication. By partitioning and distributing kernel matrix multiplies, we demonstrate that an exact GP can be trained on over a million points in 3 days using 8 GPUs and can compute predictive means and variances in under a second using 1 GPU at test time. Moreover, we perform the first-ever comparison of exact GPs against state-of-the-art scalable approximations on large-scale regression datasets with $104-106$ data points, showing dramatic performance improvements.

I saw a cool take yesterday, that we listen to music because it's a meaningful (it has patterns) adversarial example to our neural network. If we humans fit a good model of the world through exploration (I think the free energy principle says something like that? I don't totally get it), then we need to have incentives to find examples that counter our model, since that's the way we improve. Maybe art in general exploit something like that. Except some art is more simply explained because it just exploits things that help us/our genes survive (beautiful bodies, green sceneries).

The battle for future markets and bigger market shares is in full swing. The world's most influential companies are in a steady race to develop better automated systems and, in turn, boost artificial intelligence technology – taking them ahead of their competitors. By 2020, AI is expected to turn over more than 21 billion euros worldwide. However, further development of machine learning and artificial intelligence technologies seems to be blocked by a major obstacle: data privacy. The more data is consumed, the better these computer algorithms can recognize and capture patterns in the data.

The IEEE International Conference on Robotics and Automation (ICRA) is being held this week in Montreal, Canada. It's one of the top venues for roboticists and attracts over 4000 conference goers. Andra, Audrow, Lauren, and Lilly are on the ground so expect lots of great podcasts, videos with best-paper nominees, and coverage in the weeks and months ahead. For a taste of who is presenting, here is the schedule of keynotes. It also looks like you can navigate the program, read abstracts, and watch spotlight presentations by following these instructions.

Measles, once thought to have been eliminated in the U.S., is popping up in isolated outbreaks as a result of skipped well-child visits and parents' fears that the measles-mumps-rubella (MMR) vaccine is linked to autism. Though some 350 measles cases occurred in 15 states in the first three months of 2019, more than half were in Brooklyn, N.Y., and nearby Rockland County, N.Y., where large religious communities have adopted anti-vaccine positions. Rockland County responded by pulling 6,000 unvaccinated children out of schools and barring them from public places. The county's actions were effective; in just a few months, 17,500 doses of MMR were administered to area children. Yet, wouldn't it have been better to contain the outbreak before it got started?

If you're in HR, you've undoubtedly heard two buzzwords as much or more than any others in the past few years: AI and employee experience. They're topics we often link to automation -- after all, the employee experience is a lot better when you're freed from tedious, repetitive processes. But AI is actually improving the employee experience in other ways that are often overlooked. We spoke with two leading AI experts about a few ways AI is making the employee experience better than ever. Let's consider employee experience for a moment.

AND Technology Research are excited to be participating in a round table event later this month to discuss Artificial Intelligence (AI) and how Machine Learning (ML) techniques are transforming business. Nicola Thorn, Director at AND, is joining academics and industry experts to speak about the fundamentals of Machine Intelligence and how data is utilised to improve business processes and much more. Nicola will be speaking about the fundamentals of data capture and how this effects business driven IoT. Her talk will focus on the three key aspects for capturing meaningful and valuable data, both of which are paramount to the work AND undertakes regularly for their customers. Her talk will draw upon working examples of novel machine learning techniques, which AND have created and developed for digital applications.

Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it? Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice. You'll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more.

The condition is the leading cause of cancer-related death in the U.S., and early detection is crucial for both stopping the spread of tumors and improving patient outcomes. As an alternative to chest X-rays, healthcare professionals have recently been using computed tomography (CT) scans to screen for lung cancer. In fact, some scientists argue that CT scans are superior to X-rays for lung cancer detection, and research has shown that low-dose CT (LDCT) in particular has reduced lung cancer deaths by 20%. These errors typically delay the diagnosis of lung cancer until the disease has reached an advanced stage when it becomes too difficult to treat. New research may safeguard against these errors.