Update:
Fixed link
I just watched https://m.youtube.com/watch?v=s7DqRZVvRiQ lottery ticket paper author and found it really helpful before going into details of the paper
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Did you post the right video?
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No, the URL link is to a celebrity interview show. Don’t get me wrong, it’s still an interesting video! But, @Kasianenko Could you please post the correct link? Thanks!
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https://sjvasquez.github.io/blog/melnet/
Astonishingly great generated audio signals (voice and instruments), nicely presented in a gallery.
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Exploring Randomly Wired Neural Networks for Image Recognition
Paper:
And a good read:
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Efficient Learning of data augmentation policies
1000x Faster Data Augmentation
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Selfie: Self-supervised Pretraining for Image Embedding
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This is also seems interesting " Text-based Editing of Talking-head Video"
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Very cool 
Btw this is the associated paper which generated these melspectograms
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Nothing to implement here, but some things worth thinking about. I posted about it on the forum already (without much response) but now this paper was picked up in Andrew Ngs weekly newsletter, so maybe some more people will think it’s worth having a look:
Hi this is an excellent thread!
I have a tons of interesting paper that I want to go through but I hesitate to post them so that I don’t flood the thread.
I was wondering however if anyone else is interested in domain adaptation (supervised or unsupervised). I am currently focusing on this area and for that reason I collected the latest CVPR papers on it that looked promising. I am going over them as we speak but I would love to work with others on trying to implement some of their ideas.
Let me know if this sounds interesting to any of you and we can maybe do a working group!
p.s. if the category of domain adaptation is interesting to this wiki let me know and I will post my review so far.
Kind regards,
Theodore.
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Hello @Gabriel_Syme , thank you for this post. Looking forward to reading your review findings on domain adaptation. thanks Hari
Sure!! domain adaptation could be interesting for someone. So please feel free to post the papers who liked most, possibly the most recent ones. And add your review as well. If they get many “likes” we will put in the wiki.
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At ICML workshop “Climate change: How can AI help?”
Andrew Ng will speak on “Tackling climate change challenges with AI through collaboration” livestreaming at 9:45 Pacific time!
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I already posted in another thread the related paper but I’ll repost it here…
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Scheduled speakers for the workshop:
https://icml.cc/Conferences/2019/Schedule?showEvent=3507
Edit: link I posted previously to the recordings doesn’t work now
Hey, have any of you all seen this SciHive twitter. It’s a new free, open-source service (I’m not connected in any way) that allows you to read Arxiv papers and highlight stuff, comment, ask questions…etc. It looks really cool.
It has some nice features too like hovering over an acronym shows what it stands for, hovering over a reference shows the paper and name. I think it’d be incredible to have the papers we all read as individuals collectively annotated with questions, answers, additional resources…etc. Let me know if there’s a similar service you already use to do this as well. Cheers.
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Winning solution for some of the FGVC challenges at CVPR2019, plus SotA on Stanford Cars.
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New SOTA on Imagenet… 
85.4% when pretraining on Instagram and finetuning on Imagenet.
Edit: I guess it’s from last year, but they just published the models?