Or general NN based 3D object generation and testing?

Hello Everyone,

This is to share the code of a recent work “Learning Finite State Representations of Recurrent Policy Networks”.

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Abstract: Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features. In this paper, we introduce a new technique, Quantized Bottleneck Insertion, to learn finite representations of these vectors and features. The result is a quantized representation of the RNN that can be analyzed to improve our understanding of memory use and general behavior. We present results of this approach on synthetic environments and six Atari games. The resulting finite representations are surprisingly small in some cases, using as few as 3 discrete memory states and 10 observations for a perfect Pong policy. We also show that these finite policy representations lead to improved interpretability.

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Paper: https://openreview.net/forum?id=S1gOpsCctm

Source Code: https://github.com/koulanurag/mmn

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Hopefully, It will be helpful to others.

I have been working on a binary classification problem the past few months with over 200 features (all scalar data). I’m using dense neural networks with Keras. I obviously want to trim this down and have been researching didn’t tools to assist with feature selection.

So far I’ve used the K-S test, but I’m weary that a feature having different distributions between classes doesn’t necessarily mean that it will help the network differentiate between the 2 classes (or maybe it does? Unsure about this.)

While perusing Kaggle the other day I came across a user using the feature importances attribute from sklearn.ensemble.RandomForestClassifier. I haven’t been able to find out how exactly this works – does anyone know how this works and whether or not the feature importance for a Random Forest Classifier would also translate to use with neural networks?

Another thing I have been playing around with is just feeding every feature into the network and using dropout to have the network to select which features to use or discard.

Anyways, does anyone have any knowledge/advice about the merits or drawbacks of any of these strategies, or any advice for a different feature selection strategy to use? Thanks!

Desperate student looking for some help. I am working on ML based tool in field of Bioinformatics. Subject is about prediction model for cancer. I have dataset containing mutations of cells, and problem is that number of columns on rows differ. Don’t know how to approach this issue… Pls help

In Beta-VAE paper (https://openreview.net/pdf?id=Sy2fzU9gl), the authors mentioned that having Beta > 1 helps the network in learning independent latent representations. However, in VAE, the posterior distribution itself is assumed to be a Gaussian with a diagonal covariance matrix, i.e.

q(z|x) = N(U(x),Cov(x)) where Cov(x) is a diagonal matrix.

This means that we are inherently generating latents that will be independent given an input image x. So why does increase learning pressure on the KL divergence term between posterior and Gaussian prior should help any more in learning independent latents when posterior is already assumed to be independent?

In what other cases can this method of machine learning be used instead of “slow” Neural Nets?

Hey everyone,

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I’m currently working to setup an installation at a party and I’m missing one key element for it: a 12 hour long Bach chorale generated by DeepBach. It will then be played in Ableton live with some little MAX tricks I’ve hacked together and a controller hidden inside a piece of furniture to make it interactive to passers by.

I’ve spent now too many hours trying to get it running on my machine but to no avail, it seems the little IT training I got some 15 years ago won’t do the trick here…

Since I’m guessing some people here run these algos for breakfast, I figured I could ask politely here and perhaps some kind soul would pity me and and generate such a mid file? I know this is a long shot, but at this point, why not try 🙂

You will be mentioned on the installation plaque and rewarded with pictures of amazed party-goers!

That would likely require a rewrite of the DeepDream code, so it would work on the 3D recognition (convolution-based) systems that would output models, and also it might use an [heavily] edited version of the videoify.py that would progressively build up the depth of the scene (instead of zooming-in, it would be the Z axis in 3D space..). I noted the Places database, because i don’t think the standard Inception would produce good results with this..

I’m looking to buy a PC mainly to do reinforcement learning and light gaming. I know its better to build but unfortunately that isn’t an option for me. My budget is around $1800 CAD ($1336 USD). What’s the best bang for my buck?

So I am choosing between the following options to continue education. I am international student from a less known US state school.

CMU – unfunded ML thesis masters

top 20 school – funded through TA

R2 school – fellowship PhD

I’d appreciate any suggestions for my case and your experience in general.