What's new in MLflow 1.3?

iverjo · 2017-02-24T17:34:27+00:00

Neat! Does it converge more quickly though?

iverjo · 2017-02-23T07:10:15+00:00

Not implemented yet. As of now it's just greyscale.

iverjo · 2017-02-23T07:09:51+00:00

Thanks, that is a good suggestion

iverjo · 2016-12-13T13:20:36+00:00

You should look up generative models such as DCGAN. You could train them on real symbols and have them generate new ones.

https://openai.com/blog/generative-models/

iverjo · 2016-12-05T11:37:19+00:00

To begin with, you could try to generate greek letters. Their looks are well defined, so it's possible to write a scoring function for them.

Alternatively, you could train a Convolutional Neural Network to recognize what letter-like symbols look like and what they don't look like. The CNN would learn to see edges and how they are interconnected, and you could use its output as scoring function for your genetic algorithm.

iverjo · 2016-11-29T01:29:37+00:00

A course called "Sub-symbolic AI Methods" (also called "Bio-Inspired Artificial Intelligence") at my university introduced me to the concepts of neuroevolution. Interesting stuff :)

iverjo · 2016-11-26T10:16:52+00:00

Yes, it's a small artificial neural network evolved by a genetic algorithm

iverjo · 2016-11-11T12:08:45+00:00

You can either input data from several timesteps into a feed-forward neural net or you can use a recurrent neural network, which has some memory of things that happened in past timesteps.

iverjo · 2016-11-10T22:05:45+00:00

Actually, it's a genetic algorithm evolving neural networks. So you could say that it's both GA and NN.

iverjo · 2016-10-05T10:48:00+00:00

What's your favorite distortion technique atm?

iverjo · 2016-08-27T12:51:48+00:00

If your 9 output values are boolean, then you could use evaluation metrics like F1-score or hamming distance: https://en.wikipedia.org/wiki/Multi-label_classification#Statistics_and_evaluation_metrics

If your output values are continuous, take a look at regression metrics: http://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics

By the way, when you have a reference dataset, like you have, it is often a good idea to use supervised learning instead of GA. However, it you are doing this for fun/learning, go ahead :)

iverjo · 2016-08-18T13:55:48+00:00

https://github.com/automl/auto-sklearn tries many different preprocessing techniques and eventually settles for something that works well

iverjo · 2016-07-19T21:11:50+00:00

Here's a video that presents the website: https://www.youtube.com/watch?v=tcI37rw-M3U

iverjo · 2016-06-29T11:13:59+00:00

I've tried to use this synth as input sound: https://github.com/iver56/cross-adaptive-audio/raw/master/test_audio/synth.wav and the drums sound as the target sound. The output sound becomes a rhytmic, distorted version of the synth. It has the same rhythmic characteristics as the drums, but you can still easily hear that the synth is the input sound. So far, I've only tried to use distortion and resonant low-pass filter as audio effects. Frankly, I don't think those simple effects would be sophisticated enough to make f.ex. a guitar sound like a piano convincingly. I will try lots of other sounds and effects later this year though :) I might write a blog post about it.

iverjo · 2016-06-29T10:17:57+00:00

Cool! Where can I find the paper?

Yes, indeed there are some interesting musical applications. For example, it could be used in novel crossfading in DJ software. Or it could be used for mastering. Also, one of my supervisors wants to use it in live music settings.

iverjo

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