A tutorial introduction to the minimum description length principle · HackerPapers discussions

A tutorial introduction to the minimum description length principle — mentioned by cute_boi

Tue, 07 Jul 2026 19:03:23 GMT

No need stupid moving texts. CS231n: Convolutional Neural Networks for Visual Recognition - https://cs231n.github.io/ The Unreasonable Effectiveness of Recurrent Neural Networks - https://karpathy.github.io/2015/05/21/rnn-effectiveness/ Understanding LSTM Networks - https://colah.github.io/posts/2015-08-Understanding-LSTMs/ ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... Deep Residual Learning for Image Recognition - https://arxiv.org/abs/1512.03385 Multi-Scale Context Aggregation by Dilated Convolutions…

Paper and discussion history. A mention is not an endorsement.

A tutorial introduction to the minimum description length principle — mentioned by gooob

Tue, 07 Jul 2026 18:54:32 GMT

https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436... https://papers.nips.cc/paper_files/paper/2012/file/c399862d3... https://arxiv.org/pdf/1512.03385 https://arxiv.org/pdf/1511.07122 https://arxiv.org/pdf/1603.05027 https://arxiv.org/pdf/1409.2329 https://arxiv.org/pdf/1512.02595 https://arxiv.org/pdf/1409.0473 https://arxiv.org/pdf/1506.03134 https://arxiv.org/pdf/1706.03762 https://nlp.seas.harvard.edu/annotated-transformer/ https://arxiv.org/pdf/1410.5401 https://arxiv.org/pdf/1706.01427 https://arxiv.org/pdf/1806.01822…

Paper and discussion history. A mention is not an endorsement.

A tutorial introduction to the minimum description length principle — mentioned by jackhurwitz

Thu, 12 Feb 2026 16:24:13 GMT

I'm doing my PhD in compression-based machine learning, wanted to contribute a few clarifying points. The relationship between probabilistic modeling and lossless compression is very direct. A model that predicts the next symbol with probability p can on average losslessly compress that symbol with the help of an entropy coder (e.g. arithmetic coding) in -log(p) bits. Therefore improved probabilistic models immediately translate into improved lossless compressors. There are two ways people have used compressors for ML: the first is based on the Minimum Description Length (MDL) principle [0]…

Paper and discussion history. A mention is not an endorsement.

A tutorial introduction to the minimum description length principle — mentioned by is_true

Sat, 18 May 2024 12:26:29 GMT

a backup just in case https://nlp.seas.harvard.edu/annotated-transformer/ https://scottaaronson.blog/?p=762 https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://arxiv.org/pdf/1409.2329.pdf https://www.cs.toronto.edu/~hinton/absps/colt93.pdf https://arxiv.org/pdf/1506.03134.pdf https://proceedings.neurips.cc/paper_files/paper/2012/file/c... https://arxiv.org/pdf/1511.06391.pdf https://arxiv.org/pdf/1811.06965.pdf https://arxiv.org/pdf/1512.03385.pdf https://arxiv.org/pdf/1511.07122.pdf https://arxiv.org/pdf/1704.01212.pdf…

Paper and discussion history. A mention is not an endorsement.

A tutorial introduction to the minimum description length principle — mentioned by klunger

Sat, 18 May 2024 11:38:56 GMT

The headline belies the scope of this task. If you click in to see what these are: - one is a full course in convolutional neural networks https://cs231n.github.io/ - one is a 500 page text book https://www.lirmm.fr/~ashen/kolmbook-eng-scan.pdf - another one is an 80 page text book (?) https://arxiv.org/pdf/math/0406077 and so on. I would guess it is possible to go through this list if you make it your full time job for a year or so. That might be worth it depending on your priorities. Regardless, it bears mentioning.

Paper and discussion history. A mention is not an endorsement.

A tutorial introduction to the minimum description length principle — mentioned by kevgnulldev

Fri, 25 Oct 2013 17:25:48 GMT

Of related interest: http://mitpress.mit.edu/books/minimum-description-length-pri... http://arxiv.org/abs/math/0406077

Paper and discussion history. A mention is not an endorsement.