This is a place to share machine learning research papers, journals, and articles that you’re reading this week. If it relates to what you’re researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you’ve read.

Please try to provide some insight from your understanding and please don’t post things which are present in wiki.

Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links.

Previous weeks :

Most upvoted papers two weeks ago:

/u/nobodykid23: Feature-wise transformations

Besides that, there are no rules, have fun.

It’s summer. I’m done with my first semester of SE, so let’s talk about ML books that we’re currently studying. My college doesn’t offer ML, nor does it offer any good math classes so if you’re beginner like me, you have to study 24/7 and fail at converting formulae to code so many times you get literally fed up with this crap. I’m studying:

1- My friend’s Computational Linguistics dissertation. I’ve known her for along time, and helped her with Python since she wasn’t a programmer. Now she does NLP better than I do. She does research, and I can’t even understand her dissertation. Partly because it’s in Persian. I used to be an English lit student like her, but I hated English lit. I originally wanted to study CS, but I failed the SATs, so I had to apply to English lit if I wanted to go to a good college. But then I said “fuck it!” and dropped out and applied to community college. Such is life in the third world! Anyways…

2- Foundations of Machine learning by MIT Press. It’s such a great book. It has an unhealthy obsession with PAC though.

3- Introduction to Deep Learning by Eugene Charniak: Oh my God I love this book! So simple! So nifty! It’s obvious it’s been typeset by the same person who did Foundations of et al, because they look practically the same. And both have been published by MIT Press, although Charniak teaches at Brown.

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So what are you studying?

https://www.biorxiv.org/content/10.1101/582643v1

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r/compmathneuro discussion here.

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Abstract:

Over the last decade, artificial neural networks (ANNs), have undergone a revolution, catalyzed in large part by better tools for supervised learning. However, training such networks requires enormous data sets of labeled examples, whereas young animals (including humans) typically learn with few or no labeled examples. This stark contrast with biological learning has led many in the ANN community posit that instead of supervised paradigms, animals must rely instead primarily on unsupervised learning, leading the search for better unsupervised algorithms. Here we argue that much of an animal’s behavioral repertoire is not the result of clever learning algorithms—supervised or unsupervised—but arises instead from behavior programs already present at birth. These programs arise through evolution, are encoded in the genome, and emerge as a consequence of wiring up the brain. Specifically, animals are born with highly structured brain connectivity, which enables them learn very rapidly. Recognizing the importance of the highly structured connectivity suggests a path toward building ANNs capable of rapid learning.

How would you approach this concept: I am interested in making a bot that creates tattoos. I have a tattoo artist who is willing to potentially sacrifice his artistic integrity and feed the bot his simple designs to serve as a dataset. Which framework do you reckon would be best? Let’s say we have 1000 individual high res scans of black tattoos on white background and we want the bot to spit new, artificially synthesized designs (even if abstract and nonsensical). Any help would be much appreciated. This is for a short film. We have backing behind this so if there is anyone interested in being part of a creative collaboration and potentially get some small buck for your time, hit me in the DMs.

Do softmax probability outputs in classification actually mean anything? Given that NNs aren’t generative models, how can they give a probabilistic estimate of the class given a sample?

Should they be trusted if they don’t actually mean anything?

If I want to perform some computer vision task using CNN (let’s say segmentation), how can I ensure that I can get generalization across images that were captured using another camera(with different intrinsic matrix)? I tried using a learned semantic segmentation model on my own camera images and it failed terribly. I suspect this is because the convolution filters are overfitted to the particular resolution (fx, fy) we trained our model on.

I’m sorry if the question is too vague (or too stupid). I’m trying to build a very basic speech recognition (for my own learning). I’m using pyaudio to record from the microphone and I’ve managed to convert the bytes to 16bit representations. But I do not know how to move forward with my little project.

I did find bits and pieces of code that seems to do what I’m trying to achieve but doesn’t explain clearly why it does what it does.

For context I’ve previously worked on Image recognition, object detection and similar computer vision projects, but I’m a newb when it comes to audio.

Any help is appreciated.

Is there any good papers / surveys on transfer learning / domain adaptation for time series forecasting?

I am mostly working with LSTM recurrent neural networks, but any other ML-based approach should be an interesting read.