Hi all,

I am reading about Natural gradient descent. Usually I find it quite helpful if I can get to read quite a bunch of papers concerning the topic, I am figuring out good papers online, but if someone from the community can point me to some good papers, it would be great.

​

Can some one point me to a list of good natural gradient descent papers which would address the following:

1. Starting from the S Amari’s paper to the recent papers.
2. Natural gradient descent fisher approximation papers
3. Natural gradient descent papers which highlight benefits in preventing saddle points/ achieving local minima.
4. Hessian matrix approximation papers
5. Other good papers covering techniques like KFAC, hessian matrices, fisher matrices, their approximations, NSGD

​

Thanks !!

I’m wondering if there are any projects in Economics that can make use of machine learning. I am really wondering if any social science can benefit from machine learning, and how. Thanks in advance

Hi All —

​

I’m working on some scientific research code for a neuroscience experiment where we need to do density estimation in real time or something close to real time. The data is from multiple time series and it’s not particularly wide, but it is long (~7 million time points, maybe more at some point) and there is potential for up to 1000+ time series.

​

I was wondering if there were suggestions from the r/MachineLearning community on how to scale this analysis. More specifically, are there software frameworks that would make this easier? Are there alternatives to Gaussian Mixture Models and Kernel Density Estimates (i.e. some sort of neural network density estimation) that would be easier to throw on a GPU because of an existing software package?

​

I realize the answer might be no and I’m going to have to write a bunch of custom code, but I thought I might as well check before I go too far down one rabbit hole. Thanks!

A response to Rich Sutton’s “The Bitter Lesson” from Max Welling, Amsterdam, April 20 2019:

https://staff.fnwi.uva.nl/m.welling/wp-content/uploads/Model-versus-Data-AI-1.pdf

https://goo.io/63JCrv

Here is my quick demo of landing a rocket, inspired by SpaceX’s recent landings.

The rocket is released from random positions, and uses a small neural network to control its landing. The network was trained with reinforcement learning in a separate program.

Hey all,

I’m a new MS graduate trying to find work in ML in the Silicon Valley, and I need to bolster my resume. I read that I can substitute experience with quality projects, and while I have a number of those, they are all done on my own with no outside institution evaluating them. I figured a Kaggle project would be a perfect way to show that I know my stuff.

Anyone in industry have a particular challange that they like to see on a resume? Any challenges that I should avoid? I prefer working in NLP.

As I understand it (and please correct me if I’m wrong), inverse reinforcement learning + reinforcement learning will eventually produce the same result as supervised learning/behavioural cloning. Inverse RL assumes the agent’s behaviour is optimal, so it will end up just imitating the agent.

Let’s say you want to do a task better than the agent. Has there been any research on deriving a reward function from agent behaviour without assuming the agent’s behaviour is optimal?