【AI Base】Ilya Sutskever 27篇必读论文分享清单

* [ ] 1.The Annotated Transformer (nlp.sea- s.harvard.edu) 
* [ ] 2.The First Law of Complexodynamics (scottaaronson.blog)
* [ ] 3.The Unreasonable Effectiveness of RNNs (karpathy.github.io) 
* [x] 4.Understanding LSTM Networks (colah.github.io) 
* [ ] 5.Recurrent Neural Network Regularization (arxiv.org) 
* [ ] 6.Keeping Neural Networks Simple by Minimizing the Description Length of the Weights (cs.toronto.edu)
* [ ] 7.Pointer Networks (arxiv.org) 
* [ ] 8.ImageNet Classification with Deep CNNs (proceedings.neurips.cc) 
* [ ] 9.Order Matters: Sequence to sequence for sets (arxiv.org) 
* [ ] 10.GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism (arxiv.org) 
* [ ] 11.Deep Residual Learning for Image Recognition (arxiv.org) 
* [ ] 12.Multi-Scale Context Aggregation by Dilated Convolutions (arxiv.org) 
* [ ] 13.Neural Quantum Chemistry (arxiv.org) 
* [x] 14.Attention Is All You Need (arxiv.org) 
* [ ] 15.Neural Machine Translation by Jointly Learning to Align and Translate (arxiv.org) 
* [ ] 16.Identity Mappings in Deep Residual Networks (arxiv.org)
* [ ] 17.A Simple NN Module for Relational Reasoning (arxiv.org)  
* [ ] 18.Variational Lossy Autoencoder (arxiv.org)  
* [ ] 19.Relational RNNs (arxiv.org)  
* [ ] 20.Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton (arxiv.org)  
* [ ] 21.Neural Turing Machines (arxiv.org)  
* [ ] 22.Deep Speech 2: End-to-End Speech Recognition in English and Mandarin (arxiv.org)  23.Scaling Laws for Neural LMs (arxiv.org)  
* [ ] 24.A Tutorial Introduction to the Minimum Description Length Principle (arxiv.org)
* [ ] 25.Machine Super Intelligence Dissertation (vetta.org)  
* [ ] 26.PAGE 434 onwards: Komogrov Complexity (lirmm.fr)  
* [ ] 27.CS231n Convolutional Neural Networks for Visual Recognition (cs231n.github.io)