Stumbled upon the paper on the learning schedule for super-convergence by using very large learning rates by Leslie Smith and it blew my mind, and I was immediately thinking about what other papers exist that’ve made wonderous experimental discoveries in ML/DL that were never published due to them being, well, experimental in nature – and apparently these sort of papers are a no-no in the DL scientific community (at least for publication purposes).

Thus wondering; does anyone know what’s the best place to find more of these sort of papers / discoveries?

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/LazyAnt_: The Curious Case of Neural Text Degeneration

/u/sam_does_things: Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

Besides that, there are no rules, have fun.

Hey all,

Here are some of our first successful results for running OgmaNeo on Atari Pong (from pixels).

While the model trained in the post was not trained on a Raspberry Pi, we have done tests to show that it does run on the Pi in real-time (60fps) with learning enabled. If enough people would like to know exactly how fast it runs on a Pi, we can perform another experiment where everything is run entirely on the Pi and report some exact performance results. For now though, we are working on releasing the demo code with documentation.

For those that don’t know, OgmaNeo is a library written in C++ with Python bindings that implements Sparse Predictive Hierarchies (SPH), a biologically-inspired and extremely fast memory prediction framework. We have long tried to implement reinforcement learning with this system, and I think we have finally found success!

When going on a Google Scholar binge, it’s really easy for me to click the link to the citing articles of the paper I’m reading, then want to see the citing papers of those articles, and so on.

What initially looked like a small field of knowledge that would take an afternoon to get caught up on is revealed to be an unfathomable ocean that requires a lifetime of study to make any dent in. I very quickly become overwhelmed, and anxiety/panic starts to set in.

Is there any way to cope with this feeling when doing research? I suspect a lot of it is due to my ADD and desire to Learn Everything.

I have a dataset of 8 million unique members, approximately 800 million records. Of those 8 million members I have a positive sample of about 25000. It’s a binary classification problem. I would like to not simply downsample although the downsampled RF performs pretty well. The data is on a Hadoop cluster. I only have access to it via a Zeppelin notebook with PySpark. It’s a pain in the ass to get approval for packages installed. PySpark is even in python 2.7 and I don’t really use Python 2. What should I do? The notebook is in a VM that’s not connected to the worldwideweb. I would have to rewrite solutions like SMOTE if I wanted to use it. I found a package but it takes like a week for approval and I only have two more weeks for the project. I wanted to use a balanced or weighted random forest but I don’t see a native spark.ml implemention. I’m also kind of new to spark.

Any tips or advice on how to proceed? Would highly appreciate.

Though it is somewhat absurd to find ‘SOTA’ algorithm in imbalanced learning problem,

(since there exists solutions of a different nature)

are there any good recent papers (2018~) on the “method” of dealing with imbalanced learning?

(I wandered around Google scholar, but there are mostly applications of existing methods on domain-specific problems)

I’ve recognized some generative methods like SMOTE, ADASYN, tons of GAN-based techniques, cost-sensitive approaches, transforming loss functions, learning metrics, and over/under samplings, etc.

Among such categories, or other approaches that I don’t know yet, what is the most generally well-working algorithms? (papers?)

Thank you all, in advance.