Related Paper: Efficient Neural Audio Synthesis

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I have been reading the sections relating to Subscale WaveRNN in where the DeepMind team was able to generate B samples in a single step. They have discussed about conditioning a particular sample using past samples and up to F samples from the previous sub-tensors future context. In their case, they used a masked dilated CNN (this can be found on the last paragraph of 4.1 Subscale Dependency Scheme). Here’s the excerpt specifically to this:

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The Subscale WaveRNN that generates a given sub-tensor is conditioned on the future context of previous sub-tensors using a masked dilated CNN with relus and the mask applied over past connections instead of future ones.

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My first question is: how could a masked dilated CNN help with this?

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Next, Nal Kalchbrenner has tweeted this quick demo of the Subscale WaveRNN. This one confuses me a lot when I’m referring back to the original paper.

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My final question is: does anyone have taken a look at subscaling more closely?

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Any insights would be appreciated.

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(Note: This is my first post and I am hoping that I followed the format correctly.)

Link: http://www.taipeitimes.com/News/front/archives/2019/11/02/2003725093

The Ministry of Foreign Affairs yesterday protested after China forced the organizers of the International Conference on Computer Vision (ICCV) in South Korea to change Taiwan’s status from a “nation” to a “region” in a set of slides.

At the opening of the conference, which took place at the COEX Convention and Exhibition Center in Seoul from Tuesday to yesterday, the organizers released a set of introductory slides containing graphics showing the numbers of publications or attendees per nation, including Taiwan.

However, the titles on the slides were later changed to “per country/region,” because of a complaint filed by a Chinese participant.

“Taiwan is wrongly listed as a country. I think this may be because the person making this chart is not familiar with the history of Taiwan,” the Chinese participant wrote in a letter titled “A mistake at the opening ceremony of ICCV 2019,” which was published on Chinese social media under the name Cen Feng (岑峰), who is a cofounder of leiphone.com.

The ministry yesterday said that China’s behavior was contemptible and it would not change the fact that Taiwan does not belong to China.

Beijing using political pressure to intervene in an academic event shows its dictatorial nature and that to China, politics outweigh everything else, ministry spokeswoman Joanne Ou (歐江安) said in a statement.

The ministry has instructed its New York office to express its concern to the headquarters of the Institute of Electrical and Electronics Engineers, which cosponsored the conference, asking it not to cave in to Chinese pressure and improperly list Taiwan as part of China’s territory, she said.

Beijing has to forcefully tout its “one China” principle in the global community because it is already generally accepted that Taiwan is not part of China, she added.

As China attempts to force other nations to accept its “one China” principle and sabotage academic freedom, Taiwan hopes that nations that share its freedoms and democratic values can work together to curb Beijing’s aggression, she added.

Hi everyone,

I have created a simple concept: “Train an AI to answer everyday questions and upload the simulations online”

I already created the first project/simulation a couple of months ago, but I’m now looking for the next questions we could answer using AI. I have a few ideas like:

“A.I. Learns: Is it better to walk or run in the rain?”

“A.I. Learns: How to make a profit from cryptocurrency”

etc…..

Topics could be serious or lighter and funny sometimes 🙂

So, what are some interesting questions we can answer using AI?

I’ll pick up interesting answers for my next project.

Thank you.

Hey everyone,

We recently put together this blogpost, to show you how to convert a NN model from TensorFlow Lite to CoreML

Hope folks find something helpful here!

https://blog.xmartlabs.com/2019/11/22/TFlite-to-CoreML/

Hi r/MachineLearning, we were tired of constantly having to search Google, StackOverflow, and GitHub for code examples and API documentation when writing code. We built WindHive.ai, a smart coding assistant that provides you contextually relevant code snippets and docs directly from your editor!

WindHive does this by using machine-learned code representations/embeddings. We have trained neural networks on hundreds of publicly available code repositories to create embeddings for the task of deciding which snippets of code would be most useful to show you at any given time. Using these embeddings, we are able to index and search through tens of thousands of code snippets and show you exactly what you need.

We believe WindHive can help you increase your programming productivity and avoid reinventing the wheel! Please to to windhive.ai to find out more and share your feedback with us!

https://towardsdatascience.com/an-open-source-stack-for-managing-and-deploying-models-c5d3b98160bc

In this tutorial, we’re going to use DVC to create a model capable of analyzing StackOverflow posts, and recognizing which ones are about Python. We are then going to deploy our model as a web API, ready to form the backend of a piece of production software.

DVC stores your model weights and training data in a centralized location, allowing collaborators to get started easily, while also tracking changes and ensuring an accurate version history.

As a final step in this tutorial, we’re going to integrate DVC with another open source tool—Cortex—that allows us to deploy DVC-generated models as web APIs, ready for production.

Any interesting papers pushing the boundaries of creating well calibrated uncertainties with neural networks (with minimal computational expense ideally)?