Paper: https://arxiv.org/abs/1905.01019

We analyze adversarial examples using techniques from high-dimensional geometry. We demonstrate several problems with the standard formulation of adversarial training using Lp-balls centered on the data, which occur in high-dimensional space. From these insights, we propose a new geometric constraint to replace the Lp-balls which gives improved robustness in some settings.

I’m the lead author of this article and would be happy to answer any questions!

Hello I’ve made a discord server for beginners and experts to talk about the newest tech and the newest things in the software and AI industry. For example me, I work on a website that is all about quantitative finance, I use different ML and DL models there and also different statistical models. Curious ?

For more, just join the server: https://discord.gg/Wx9wXa4

Thanks for reading 🙂

Want to hear your thoughts about this. As far as i know, this machine has learned everything itself. No innate knowledge (except the rules?). So one can argue that this machine basically has no innate priors?

TensorFlow Graphics is out! This new library brings 3D and graphics functionalities to TensorFlow!

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Blog post announcement: https://medium.com/tensorflow/introducing-tensorflow-graphics-computer-graphics-meets-deep-learning-c8e3877b7668

GitHub: https://github.com/tensorflow/graphics

I have just completed Deep Learning Specialization of Andrew Ng and I have previously attended a summer school on Machine learning . I have not done any other project or course . Companies like Amazon , Google , Goldman Sachs will start coming for hiring interns in about 2 months in my college . What should I do in these two months to get an internship ?

I’ve been working on a project for the past year using deepfake faceswap to bring Salvador Dali “back to life” on some interactive screens at the Dali Museum.

There’s an article about the project in The Verge and this behind the scenes video explains the project.

This is done with a mix of faceswap results from both the dfaker and faceswap repos on GitHub. Results are further refined in After Effects. Our archival source footage of Dali used to train the network is 60 years old and shot in black and white, so in addition to the faceswap, we’re colorizing the results.

We tried using ML to recreate Dali’s voice as well, but the results just aren’t good enough yet. Someday soon, hopefully!

This project allows to easily train a CNN/RNN/CRNN on the UrbanSound8k dataset using on-the-fly Spectrogram computation in PyTorch.

The Spectrogram is computed on GPU as a layer using torchaudio_contrib, come check it out and help us improve/discuss!

Hi, not entirely sure this is the best place to ask, but I’m sure many of you are writing something for NeurIPS ATM.

Earlier this year, I wrote a paper, which I posted on ArXiv, which was then rejected from ICML in a close call. Ever since, I have addressed the concerns issued by the reviewers, and in the process, came up with something fundamentally better, but obviously within the exact same topic. I want to submit the new version for double-blind peer review and am asking for insights from the community!

Should I cite the pre-print? The pre-print was never peer reviewed and all results in the new version are better.

Can I simply reuse large portions of the pre-print (i.e. word for word)? Much of the old paper (e.g. introduction, related work) could be reused without any changes and be just as correct. However, I am afraid that a reviewer will stumble upon the preprint and either accuse me of plagiarizing (from myself, I guess), or infer that this might be a violation of double-blind review.

Happy to hear your thoughts!