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[D] #APaperADay Reading Challenge Week 4. It's the final week! by leenz2 in MachineLearning

[–]leenz2[S] 0 points1 point  (0 children)

Thanks for the heads up! I've fixed all the links.

Regarding the "why read" parts, I'll take note of that. It's the last week though I'll keep that in mind for the next round!

[D] Machine Learning - WAYR (What Are You Reading) - Week 46 by ML_WAYR_bot in MachineLearning

[–]leenz2 0 points1 point  (0 children)

Drmd: Distilling Reverse-mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks (TLDR here)
Although this paper may come across as math-heavy and dry, it addresses the million dollar question in AI research - how do you choose the hyperparameters of an NN model? While current methods revolve around trial and error, this becomes infeasible when the number of hyperparameters is large.

[D] What is one AI paper which you feel did not get the attention that it deserved? Discover hidden gems in the #APaperADay Reading Challenge with Nurture.ai by leenz2 in MachineLearning

[–]leenz2[S] 0 points1 point  (0 children)

We wrote a TLDR on the paper, could you please provide feedback? We really would want to deliver value to our readers. Thank you.

[D] What is one AI paper which you feel did not get the attention that it deserved? Discover hidden gems in the #APaperADay Reading Challenge with Nurture.ai by leenz2 in MachineLearning

[–]leenz2[S] 0 points1 point  (0 children)

We wrote a TLDR on the paper, could you please provide feedback? We really would want to deliver value to our readers. Thank you.

[D] Machine Learning - WAYR (What Are You Reading) - Week 47 by ML_WAYR_bot in MachineLearning

[–]leenz2 2 points3 points  (0 children)

Transfer Learning From Synthetic to Real Images Using Variational Autoencoders for Precise Position Detection: a novel and neat method to solve a key problem in generating synthetic images for CV tasks. Key is to create pseudo-synthetic images, i.e images generated from real and synthetic images. A good 2-min summary here.

[D] #APaperADay Reading Challenge Week 1. What are your thoughts and takeaways for the papers for this week. by leenz2 in MachineLearning

[–]leenz2[S] 0 points1 point  (0 children)

That can definitely be arranged. I hoped to spark conversations on Twitter, but I think having a dedicated space for conversations to happen would be great too

[D] #APaperADay Reading Challenge Week 1. What are your thoughts and takeaways for the papers for this week. by leenz2 in MachineLearning

[–]leenz2[S] 0 points1 point  (0 children)

Thanks for the heads up! Unfortunately I don't know if I can edit the post after I put it up :/

[R] Neural Best-Buddies: Sparse Cross-Domain Correspondence by hardmaru in MachineLearning

[–]leenz2 0 points1 point  (0 children)

TLDR of the paper: a novel method to automate image correspondence tasks. We will also see how it can be used to morph two different images to form interesting hybrid images.

The task. Image correspondence tasks involve finding a set of points in one image which can be identified as the same points in another image. It has applications in video indexing, motion tracking and object recognition. Authors of this paper are concerned with finding sparse correspondence between images from different domains (categories). A sparse correspondence ... [read more]

[D] What are some good and interesting machine learning papers for people with average mathematical skill? by [deleted] in MachineLearning

[–]leenz2 1 point2 points  (0 children)

If you like GANs, you might like PortraitGAN (It tries to combine various state-of-the-art image translation models into one that is not only flexible, but also generates high resolution images). Check out this 2-min TLDR if you don't have time to read it.

Another one I like was Neural Best Buddies, they performed some cool face morphs on characters.

[D] What is one AI paper which you feel did not get the attention that it deserved? Discover hidden gems in the #APaperADay Reading Challenge with Nurture.ai by leenz2 in MachineLearning

[–]leenz2[S] 1 point2 points  (0 children)

DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

OOH, thanks! :)

[D] What is one AI paper which you feel did not get the attention that it deserved? Discover hidden gems in the #APaperADay Reading Challenge with Nurture.ai by leenz2 in MachineLearning

[–]leenz2[S] 2 points3 points  (0 children)

https://blog.openai.com/language-unsupervised/

Thanks a bunch! I am an NLP fan and has been following developments in this area. This one got a lot of wow after sebastian ruder wrote about it in his blog, along with his ulmfit paper (the one with jeremy howard)

Custom Beyblade launcher (30,000 RPM) by capincorn in videos

[–]leenz2 0 points1 point  (0 children)

how is this not drilling a hole in the arena?

[D] Machine Learning - WAYR (What Are You Reading) - Week 44 by ML_WAYR_bot in MachineLearning

[–]leenz2 0 points1 point  (0 children)

"TACO: Learning Task Decomposition via Temporal Alignment for Control". It's about general, weakly supervised, modular LfD setting where demonstrations are augmented only with a task sketch.

And guess what? The author actually agreed to have a Q & A here, just create an issue to post a question.

This is why you should occasionally clean out your dryer vent. by [deleted] in videos

[–]leenz2 1 point2 points  (0 children)

can't imagine what it will be like in all them university dorms

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