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I'm all in favor of scaling down deep learning. Also, recently I published a paper on DNA compression that shows that deep learning is still quite easily beat i
by miltondts 6y ago
I'm all in favor of scaling down deep learning. Also, recently I published a paper on DNA compression that shows that deep learning is still quite easily beat in terms of time and compression ratio by using specialized models and simple MLPs.
Open access to the paper here: https://doi.org/10.1093/gigascience/giaa119 https://doi.org/10.1093/gigascience/giaa119
- mpfundstein 6y agowait until DeepMind gets hold of this problem :-)
- suifbwish 6y agoMeh, Deepmind is old news. Wait till the Jiuzhang team publishes its findings on this
- thamer 6y agoVery interesting work, thanks for sharing! It's great that you included a link to the project on GitHub, it's unfortunately too rare to see research papers accompanied by source code.
- miltondts 6y agoYes, this is a big problem. We want to do comparisons, and it is quite difficult to download much of the software and datasets. For example, we are in the process of writing a paper for the compression of proteins. There are a few papers about this problem, but only one working compressor that we could find. Even more problematic is when the papers in question claim very good efficiency and we are always left wondering if we are missing something, or if the data they used isn't the same data we have access to etc. In that regard, GigaScience is great, because it places a lot of importance in reproducibility.
- deleted 6y ago[deleted]