Important Papers

• The Annotated Transformer:
  
• The First Law of Complexodynamics:
  
• The Unreasonable Effectiveness of Recurrent Neural Networks:
  
• Understanding LSTM Networks:
  
• Recurrent Neural Network Regularization:
  
• Keeping Neural Networks Simple by Minimizing the Description Length of the Weights:
  
• Pointer Networks:
  
• Order Matters: Sequence to sequence for sets:
  
• ImageNet classification with deep convolutional neural networks:
  
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism:
  
• Deep Residual Learning for Image Recognition:
  
• Multi-Scale Context Aggregation by Dilated Convolutions:
  
• Neural Message Passing for Quantum Chemistry:
  
• Attention is all you need:
  
• Neural Machine Translation by Jointly Learning to Align and Translate:
  
• Identity Mappings in Deep Residual Networks:
  
• A Simple NN Module for Relational Reasoning:
  
• Variational Lossy Autoencoder:
  
• Relational recurrent neural networks:
  
• Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton:
  
• Neural Turing Machines:
  
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin:
  
• Scaling Laws for Neural Language Models:
  
• A Tutorial Introduction to the Minimum Description Length Principle:
  
• Machine Super Intelligence: