We’ve developed a method to assess whether a neural network classifier can reliably defend against adversarial attacks not seen during training. Our method yields a new metric, UAR (Unforeseen Attack Robustness), which evaluates the robustness of a single model against an unanticipated attack, and highlights the need to measure performance across a more diverse range of unforeseen attacks.

Website: https://openai.com/blog/testing-robustness/

Paper: http://arxiv.org/abs/1908.08016

Code: https://github.com/ddkang/advex-uar

What happens if you take meta-learning for vision and apply it to NLP? Prototypical Networks with lexical features perform worse than nearest neighbors on new classes. How can we do better?

arXiv: arxiv.org/abs/1908.06039

code: https://github.com/YujiaBao/Distributional-Signatures

This open-source tool allows you to create AWS instances and also takes care of all the ssh configs so you can connect to it from the command line or VS Code (using remote ssh plugin). You can choose what resources you need (CPU, GPU, memory) from the command line.

https://github.com/provisionpad

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I always find it difficult and confusing to deal with AWS, so I think this makes is a lot easier to work with AWS and abstracts the clutter away. Would be curious to hear how you guys deal with this problem.

Here is an example of working directly in an AWS EC2 instance from my local VS Code:

https://imgur.com/a/ZJMxsQl

I was looking for some frameworks for action recognition, preferably real-time ones. Additional question, how would one go about action recognition of a specific person? Say I need to detect a specific person (let’s assume he has a specific outfit) and go about recognizing his actions only. Any ideas for achieving that?

There is plenty of literature about training models. However: How can I effectively monitor my models once they are deployed and live?

Things that I would like to monitor:
– Accuracy
– Does my live input data represent my training data

Do you know any tools for monitoring these metrics? And are there other metrics that you think I should monitor?
Thanks

For bureaucratic reasons I got a Laptop for Machine Learning. Is there any benchmark tasks or code I can run to see how effective my laptop is for ML? Like that “calculate pi” tasks

In this example, an LSTM neural network is used to forecast energy consumption of the Dublin City Council Civic Offices in Ireland using data between April 2011 – February 2013.

An LSTM model was generated and run on the data, and the mean percentage error was 6.1%.

The methodology and findings can be found here. Would be grateful for any feedback.

Paper: https://arxiv.org/abs/1907.12920

Code: https://github.com/xl-sr/THOR

Video: https://www.youtube.com/watch?v=EAv7SvI3EH0&amp=&feature=youtu.be

Project: https://sites.google.com/view/vision-thor/

Hello guys,

I recently implemented a Keras code for the paper https://arxiv.org/abs/1908.04008 and I would be super glad if someone experienced would review my code and give me feedbacks ! (In terms of Python / Keras style or correctness of the implemented code…)

Here is the repo link : https://github.com/Cyril9227/Keras_IEBN

The official Pytorch code is here : https://github.com/gbup-group/IEBN

Thank’s a lot 🙂