Ciro Santilli @cirosantilli 37

This is about transactions that are interesting not because of their inscriptions, but for some other reason, such as transaction size, etc.

Related:

mlcommons.org/en/ Their homepage is not amazingly organized, but it does the job.

Benchmark focused on deep learning. It has two parts:Furthermore, a specific network model is specified for each benchmark in the closed category: so it goes beyond just specifying the dataset.

Results can be seen e.g. at:Those URLs broke as of 2025 of course, now you have to click on their Tableau down to the 2.1 round and there's no fixed URL for it:

And there are also separate repositories for each:

E.g. on mlcommons.org/en/training-normal-21/ we can see what the the benchmarks are:

The most important thing this project provides appears to be the .onnx file format, which represents ANN models, pre-trained or not.

Deep learning frameworks can then output such .onnx files for interchangeability and serialization.

Some examples:

The cool thing is that ONNX can then run inference in an uniform manner on a variety of devices without installing the deep learning framework used for. It's a bit like having a kind of portable executable. Neat.

Some interesting analysis by Parth Shukla twitter.com/pparth | www.linkedin.com/in/parth-shukla-59583b20/:Apparently most of the routers were Chinese. No surprise there.

Data format overview: opendata.stackexchange.com/questions/1951/dataset-of-domain-names/21077#21077

TODO was this data also obtained illegally like the Carna botnet

The student organized bar of the École. There's a corresponding Binet that takes care of it.

This kind of died at some point checked as of 2023.

Julian Togelius cites it e.g. at: togelius.blogspot.com/2016/07/which-games-are-useful-for-testing.html