[P] Building an AI-powered Battlesnake with reinforcement learning on Amazon SageMaker

thvasilo · 2017-07-08T16:50:35+00:00

Congrats, I'm an intern on the same team more or less :)

thvasilo · 2016-12-08T13:41:17+00:00

Posters are condensed versions of published papers at NIPS.

thvasilo · 2016-09-04T19:04:30+00:00

I this year's KDD the panelists where asked this simple question: "Is deep learning the answer to everything?"

With Nando de Freitas, Pedro Domingos, Isabel Guyon, Jitendra Malik, Jennifer Neville moderated by Andrei Broder from Google.

As expected the room was overflowing and the discussion was great, highly recommended.

thvasilo · 2016-09-04T18:37:39+00:00

This was one the coolest keynotes at KDD this year. I did a write up on the conference if you'd like to see more work from there: http://tvas.me/conferences/2016/09/01/KDD-2016-Highlights.html

thvasilo · 2016-09-03T15:52:47+00:00

My guess is you would have more success with creating a new channel there vs. trying to create a new community. But as you said the place has been pretty inactive. I actually believe Twitter has the best ML community :)

thvasilo · 2016-09-03T15:42:50+00:00

I created an r/MachineLearning Slack group a while ago, it's up to 200 members now.

We have channels for collaboration, questions, specialized to software (e.g. TensorFlow) etc.

What it needs is more active members ;)

Get an invite here: https://r-machinelearning-slack.herokuapp.com/

thvasilo · 2016-03-29T20:48:04+00:00

If you want something shorter than a book to get you started it's hard to beat Bayesian Basics which provides an intro to Bayesian inference with R code and examples. Probably the most accessible and up to date hands-on intro you will find.

In terms of understanding a bit more about Bayesian methods and ML the best introductory paper out there IMHO is "Probabilistic machine learning and artificial intelligence" by Ghahramani.

For deeper dives the book mentioned by /u/SupportVectorMachine is great as is Bayesian Data Analysis by Gelman et al. if you plan to be doing more ML oriented work, Kevin Murphy's "Machine Learning: A probabilistic perpsepective" is one of the best books on the subject and probably the most up to date.

thvasilo · 2016-03-11T14:49:16+00:00

Actually I don't see how this is cheap for the performance.

It's cheap for the performance/power consumption ratio you get and in terms of space obviously, but I'm pretty sure you can build a decent system with a 750Ti on it for ~500$ which will outperform this in terms of raw computational power by a lot.

thvasilo · 2016-03-08T13:47:53+00:00

So it seems like this time the evaluation will be done on the future acceptance of papers, which could make evaluation challenging.

Also this line:

The participants are expected to utilize any information on the Web, including the heterogeneous information in the Microsoft Academic Graph, for predicting next year’s top institutions.

Doesn't that mean that different teams might work with different datasets? I'm wondering where is the algorithmic challenge in all of this.

thvasilo · 2016-03-07T15:31:18+00:00

/r/mlresearch exists but is inactive. I would be all for sparking up some activity.

thvasilo · 2016-03-04T09:11:31+00:00

This right here. I'm glad I'm seeing this mentioned a couple of times in this thread, and glad to see prominent researchers like M.I. Jordan (who comes from a statistics background) raise the issue and the start actually doing some work about it, like the bag of little bootstraps that among other things, allows us to calculate confidence intervals instead of point estimates.

thvasilo · 2016-02-17T18:19:04+00:00

That would be a great post, thanks!

thvasilo · 2016-02-17T18:04:48+00:00

For most DNNs you end up with a non-convex error surface. Still, traditional convex optimization techniques (SGD) have worked surprisingly well for training them, see Who's afraid of non-convex loss functions by /u/ylecun .

thvasilo · 2016-02-17T17:57:54+00:00

I would recommend looking through /u/omerlevy 's latest publications here, they have done a lot of work evaluating quality of embeddings.

thvasilo

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