30papers.com – Ilya's 30 essential ML papers, in a beginner friendly format

As an aside, I've seen folks mention respecting reduced animation hints and such in the past and was always curious about this because I've never had any negative experiences with animations... until now!

Something about the animations on this site did my brain in while scrolling through the papers, and now I "get it."

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

https://arxiv.org/pdf/1704.01212

https://arxiv.org/pdf/2001.08361

https://arxiv.org/pdf/1811.06965

https://www.cs.toronto.edu/~hinton/absps/colt93.pdf

https://arxiv.org/pdf/math/0406077

https://scottaaronson.blog/?p=762

https://arxiv.org/pdf/1405.6903

https://onlinelibrary.wiley.com/doi/10.1002/047174882X.ch14 https://github.com/Bladefidz/information-theory/blob/master/...

https://arxiv.org/pdf/1611.02731

https://www.vetta.org/documents/Machine_Super_Intelligence.p...

https://karpathy.github.io/2015/05/21/rnn-effectiveness/

https://cs231n.github.io/

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 - https://arxiv.org/abs/1511.07122

Identity Mappings in Deep Residual Networks - https://arxiv.org/abs/1603.05027

Recurrent Neural Network Regularization - https://arxiv.org/abs/1409.2329

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin - https://arxiv.org/abs/1512.02595

Order Matters: Sequence to Sequence for Sets - https://arxiv.org/abs/1511.06391

Neural Machine Translation by Jointly Learning to Align and Translate - https://arxiv.org/abs/1409.0473

Pointer Networks - https://arxiv.org/abs/1506.03134

Attention Is All You Need - https://arxiv.org/abs/1706.03762

The Annotated Transformer - https://nlp.seas.harvard.edu/annotated-transformer/

Neural Turing Machines - https://arxiv.org/abs/1410.5401

A Simple Neural Network Module for Relational Reasoning - https://arxiv.org/abs/1706.01427

Relational Recurrent Neural Networks - https://arxiv.org/abs/1806.01822

Neural Message Passing for Quantum Chemistry - https://arxiv.org/abs/1704.01212

Scaling Laws for Neural Language Models - https://arxiv.org/abs/2001.08361

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism - https://arxiv.org/abs/1811.06965

Keeping Neural Networks Simple by Minimizing the Description Length of the Weights - https://www.cs.toronto.edu/~hinton/absps/colt93.pdf

A Tutorial Introduction to the Minimum Description Length Principle - https://arxiv.org/abs/math/0406077

The First Law of Complexodynamics - https://scottaaronson.blog/?p=762

Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton - https://arxiv.org/abs/1405.6903

Kolmogorov Complexity - https://onlinelibrary.wiley.com/doi/book/10.1002/047174882X

Variational Lossy Autoencoder - https://arxiv.org/abs/1611.02731

Machine Super Intelligence - https://www.vetta.org/documents/Machine_Super_Intelligence.p...

  for x in 1611.02731 1511.06391 1811.06965 1512.03385 1511.07122 1704.01212 1409.2329 1512.02595 1706.01427 1410.5401 1806.01822 1706.03762 1409.0473 1506.03134 2001.08361 1405.6903 1603.05027 math/0406077; do curl -fL https://arxiv.org/pdf/$x -o ${x##*/}.pdf; done
  for u in https://www.cs.toronto.edu/~hinton/absps/colt93.pdf https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf https://www.vetta.org/documents/Machine_Super_Intelligence.pdf https://www.lirmm.fr/~ashen/kolmbook-eng-scan.pdf https://scottaaronson.blog/?p=762 https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://colah.github.io/posts/2015-08-Understanding-LSTMs/ https://nlp.seas.harvard.edu/annotated-transformer/ https://cs231n.github.io/; do curl -fLO "$u"; done