Hello /r/machinelearning !

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I have been writing a weekly newsletter sharing interesting new papers, projects, or issues in reinforcement learning. If you are interested in reinforcement learning but find it hard to keep up with it, my newsletter might be able to help! You can subscribe and check past issues here: https://www.endtoend.ai/tags/rl-weekly/. The 19th issue was sent just a few minutes ago.

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Cheers 🙂

https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers

A curated list of gradient boosting research papers with implementations from the following conferences.

Machine learning:

1. NeurIPS
2. ICML
3. ICLR

Computer vision:

1. CVPR
2. ICCV
3. ECCV

Natural language processing:

1. ACL
2. NAACL
3. EMNLP

Data Mining:

1. KDD
2. ICDM
3. CIKM
4. WWW

Artificial intelligence:

1. AAAI
2. IJCAI
3. UAI
4. AISTATS

A technical overview of “Fluctuation-dissipation relations for stochastic gradient descent” published at ICLR 2019:

https://medium.com/@yaroslavvb/iclr-optimization-papers-i-fluctuation-dissipation-relations-for-sgd-a638ad9964cc

I’m not part of academia, so maybe this question is quite silly, but I’d love to know the answer.

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I’ve noticed a trend of ML papers containing no source code for creating the models, training on the dataset and no weights for the actual trained model on which the experiments were ran.

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How would one go about validating the research in these papers with the lacking source code ?

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For one re-constructing the model exactly as the original in some cases could be hard, since the paper might only mention the generic architecture for some block, but not the exact variation they went for.

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Secondly, it seems it would be a rather tedious task to reverse engineer the model and the training code just to validate simple things about what the researchers did (e.g. that they didn’t make some error in the sampling of train/test/validation data and as such got the wrong numbers in the paper for a specific dataset)

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Do people just email the authors and ask for the source code ? Thus keeping it constrained within the specific academic circles the author desire ? If so, why make the paper open to being with, since for a lot of them a pretty good replica of the model can be constructed from the paper alone.

If you go on arxiv, you can activate a feature called Bibex, which shows references and citations for each paper.

Here’s a description https://labs.arxiv.org/projects/bibexplorer

offtopic, but I didn’t even know arxiv had labs https://labs.arxiv.org/

Anyways, some people do not like the new feature

https://twitter.com/zacharylipton/status/1132479367425093634

I can’t say one way or another, but as someone who analyzes research papers, this data will be great to analyze and train on.

Hi all,

I came across some stuff from Vicarious-AI online, (somewhat recently, eg, see here: https://science.sciencemag.org/content/358/6368/eaag2612)

My question is simple: Why haven’t RCN’s received more attention?… Dis-regarding the Vicarious-AI itself, the technology does seem to be interesting, right? The fact that they are trying to re-think how we should design AI systems / or at least partition the problem seems to hold some water I would say…. so I was curious why this piece of _technology_ hasn’t received more attention?…

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Edit: Also – I realized that I had forgotten to add a title, so re-made the post. Thank you!

Hi all,

I came across some stuff from Vicarious-AI online, (somewhat recently, eg, see here: https://science.sciencemag.org/content/358/6368/eaag2612)

​

My question is simple: Why haven’t RCN’s received more attention?… Dis-regarding the Vicarious-AI itself, the technology does seem to be interesting, right? The fact that they are trying to re-think how we should design AI systems / or at least partition the problem seems to hold some water I would say…. so I was curious why this piece of _technology_ hasn’t received more attention?…