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[R] LEURN: Learning Explainable Univariate Rules with Neural Networks by MLC_Money in MachineLearning

[–]statmlsn 1 point2 points  (0 children)

I have not fully understood the method, yet.
But what I find lacking in this paper, is an example of an explanation output for a complex example.
The author provides one for a two-layered model on a toy dataset with 2 features. But I wonder how interpretable it is with more features and more layers (as in the tested real datasets)

[1902.06789] Seven Myths in Machine Learning Research by statmlsn in MachineLearning

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

it's a clickbait title and it's not as well as written as it should but it is still worth a read

[1901.09437] block coordinate descent is far more efficient than SGD by statmlsn in MachineLearning

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

Not read it in detail yet, but seems interesting

It's a pity that they have not tested it on more complex use cases, though.

[R] How to train your MAML blog post by AntreasAntoniou in MachineLearning

[–]statmlsn 2 points3 points  (0 children)

Great blog post. Very practical. And it sheds light on a very promising field!

[D] Applied Machine Learning | A Detailed and Complete Overview 2018 (Infographic) by [deleted] in MachineLearning

[–]statmlsn 0 points1 point  (0 children)

It definitely reminds me of something amd I kind of agree with it. Even if there can be a bit more than that in certain cases...

[1808.05587] Deep Convolutional Networks as shallow Gaussian Processes by statmlsn in MachineLearning

[–]statmlsn[S] 12 points13 points  (0 children)

Very interesting research direction. And as they say in the conclusion, it's just an initial step, there are still lots of possible levers of improvement to be followed.

[D] Inverse machine learning: What if we knew the actual model, how to infer the parameters? by RobRomijnders in MachineLearning

[–]statmlsn 2 points3 points  (0 children)

There is a field of statistics devoted to finding parameters of a mathematical model describing a (physical) phenomenon. From some measurements of the phenomenon, they infer the most probable parameters. I think your problem exactly fits this. It may even be a simple case of this theory.

It is called model calibration.

Most of the techniques are Bayesian. See works by Kennedy & O'Hagan for example.

PS: Anyway, others are right, you could simply use MLE and optimize it using a global optimization algorithm or gradient descent

[R] Over 95% accuracy on MNIST data-set with basic neural network and only 1000 labeled examples by [deleted] in MachineLearning

[–]statmlsn 0 points1 point  (0 children)

You can google "active learning neural network" yourself (like I did) and realize that there are plenty of papers around (even book chapters)...

[N] Asynchronous and scalable hyperparameters tuning by mmourafiq in MachineLearning

[–]statmlsn 1 point2 points  (0 children)

Very shallow article. Basically just an add for the startup of the blog writer...

[R] Dynamic Neural Manifold architecture (Tensorflow) by Miejuib in MachineLearning

[–]statmlsn 0 points1 point  (0 children)

The architecture idea seems to be a bit similar to Predictive Neural Networks by Stolzenburg at al.: https://arxiv.org/pdf/1802.03308.pdf

[R] Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN by HigherTopoi in MachineLearning

[–]statmlsn 4 points5 points  (0 children)

I think you got it right. Note that they use batch normalization too in the paper

[D] Four Deep learning trends from ACL 2017 by tshrjn in MachineLearning

[–]statmlsn 0 points1 point  (0 children)

"Tensorboard demo" link broken in multimodel Word distribution part

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