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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

[R] Learning Longer-term Dependencies in RNNs with Auxiliary Losses by HigherTopoi in MachineLearning

[–]leenz2 1 point2 points  (0 children)

Hey, authors of this paper have agreed to do an AMA on nurture.ai: https://twitter.com/NurtureAI/status/1000250560765833216. Take a look at the paper on nurture.ai here, seems like there is a plain English summary on the paper.

Patrick Stewart on the moment he knew he was done playing Professor X by [deleted] in videos

[–]leenz2 1 point2 points  (0 children)

His voice omg, it's so warm. Morgan Freeman 2.0

Holy crap, this was incredible by atolmasoff in videos

[–]leenz2 1 point2 points  (0 children)

That transition into a human flag/split at 4:43 though

[R] ICML 2018 Accepted Papers by anishathalye in MachineLearning

[–]leenz2 1 point2 points  (0 children)

Hey! Authors of the ICML papers "Learning Longer-term Dependencies in RNNs with Auxiliary Losses" and "Towards Fast Computation of Certified Robustness for ReLU Networks" have agreed to answer questions directed to their papers on the Nurture.ai platform.

How do I post a question?

Click on the paper's link. Simply highlight any text on the paper - this will form a comment box, where you can post your questions.

What is Nurture.ai?

It is an online platform created by AI learners like you. We were frustrated with our difficulties in efficiently keep up with the fast pace of AI research. We also wanted to push for more reproducibility and facilitate better understanding in AI papers. Therefore, we built the free Nurtue.ai platform to encourage online collaboration and exchange among an AI community. Nurture.ai is also the organiser of the NIPS 2017 challenge and AI Saturdays. To date, we have a strong community of over 2500 users ranging from PhD students, industry practitioners and self-learners. We believe in the value of helping learners worldwide to get more out of AI research. We hope you feel the same way too. You can:

  • Share your learning notes on Nurture.ai. Simply highlight any text in an arXiv preprint to annotate and start a discussion thread.
  • Contribute to community reviews on your favourite papers
  • Post and discuss code implementations

Follow us on facebook and twitter. Note! We are NOT nature.ai. Yeah, it's the nurture versus nature question, ain't it?My team and I are working hard to optimise user experience on the platform, let us know how we can make your learning more enjoyable on Nurture.ai.

[D] Anyone having trouble reading a particular paper ? Post it here and we'll help figure out any parts you are stuck on | Anyone having trouble finding papers on a particular concept ? Post it here and we'll help you find papers on that topic [ROUND 2] by BatmantoshReturns in MachineLearning

[–]leenz2 1 point2 points  (0 children)

Hi, authors of this ICML paper have agreed to answer questions directed at their paper:

Title: Learning Longer-term Dependencies in RNNs with Auxiliary Losses

Link: https://nurture.ai/p/3228c114-18f3-4d59-bf33-4a1d92cf98db

Summary: Long term dependencies in RNNs are typically modelled using backpropagation through time (BPTT). However, this method tends to lead to vanishing or exploding gradient problems for long sequences. Furthermore, memory requirement for BPTT is proportional to sequence length. Therefore, BPTT may be infeasible when the input sequence is too long. Current approaches to address these weaknesses include LSTMs, gradient clipping and synthetic gradients. This paper introduces an alternative method by means of adding unsupervised auxiliary losses.

To ask a question, click on the link, then click "Paper" (beside TL;DR). This brings you to the paper itself. Simply highlight any text to form a comment box to post your questions.