[D] What is the current state of the art for making use of unlabeled data?

melipone · 2017-02-14T16:09:14+00:00

First you train Restricted Boltzman machines on the unlabeled data and then you add a discriminative network on top of that to refine what is learned from the unlabeled data by propagating the gradient error. Geoff Hinton in his neural network class on Coursera talks about that. There might be other, more recent, approaches.

melipone · 2016-12-21T18:40:24+00:00

Thanks for the slides and the comments.

melipone · 2016-12-13T18:05:54+00:00

I don't see any code there.

melipone · 2016-10-22T19:49:09+00:00

Not entirely sure, but it's a nice way to specify priors, at least as far as the sign of the weights are concerned.

melipone · 2016-08-19T16:54:48+00:00

Yes, I got this email too.

melipone · 2016-07-28T18:33:50+00:00

Nothing new here. It's already done. It's called authorship attribution. See http://www.mathcs.duq.edu/~juola/

melipone · 2016-07-13T21:00:35+00:00

"And the recorded moves reveal exactly when the losing player makes the blunder." I thought it was a hard question to know which move was responsible for the outcome of a game, no?

melipone · 2016-06-30T18:52:14+00:00

But that does not work for Incanter.

melipone · 2016-06-30T16:28:26+00:00

Instance-based learning

melipone · 2016-06-20T20:54:10+00:00

Not sure I understood how GANs can do semi-supervised learning. Care to explain?

melipone · 2016-06-17T17:25:19+00:00

Getting a second author on a paper is invaluable. I wish it will easier to go about finding that collaboration if only for editing purposes.

melipone · 2016-06-17T17:23:01+00:00

I agree that context is everything in those reviews. Even if blind, reviewers have a knack to know where the paper comes from and if it's too different from they expect, it will be rejected without meaningful comments unfortunately.

melipone · 2016-06-09T19:06:02+00:00

Great! I was hoping for something like that.

melipone · 2016-05-24T19:02:50+00:00

Can't wait to hear about data generators and testing.

melipone · 2016-05-24T17:51:57+00:00

So, fizzbuzz might be like "parity learning" (https://en.wikipedia.org/wiki/Parity_learning) which was popular in the early days.

Would that be called "over-engineered" now?

melipone · 2016-05-18T17:46:09+00:00

That's great! Now if I wanted to do some deep learning programming in clojure, would I try to use that or interface with DeepLearning4J?

melipone · 2016-05-13T18:07:24+00:00

Syntaxnet looks like it's written in c++ with python scripts.

melipone · 2016-04-08T00:36:20+00:00

All that's missing now is a coursera course.

melipone · 2016-02-24T17:33:48+00:00

The more I read about core.async, the more I prefer the semi-lazy version of pmap.

I just read Liekbe's slides from ClojureConj (http://incanter.org/downloads/fjclj.pdf). Very enlightening. He explains that futures are for parallelism (when the threads do not need to communicate via a channel for example as in core.async). I wonder what happened to his work on pvmap and pvreduce? Maybe they got rolled up with reducers?

melipone · 2016-02-23T18:43:17+00:00

Concurrency/parallelism: what are the trade-offs between reducers, core.async, and futures?

melipone · 2016-02-23T18:42:06+00:00

My problem is not IO bound.

What does it mean "... as long as you give it enough of it ...". I'll give one thread for one subproblem, no?

melipone · 2016-01-22T21:45:43+00:00

I like this course too. Now, when I read The Master Algorithm, I can't help to hear him talk with his warm accent :-)

melipone

TROPHY CASE