Important Papers

• A Simple NN Module for Relational Reasoning: A simple neural network module for relational reasoning
  
• A Tutorial Introduction to the Minimum Description Length Principle: A tutorial introduction to the minimum description length principle
  
• Attention is all you need: Attention Is All You Need
  
• Deep Residual Learning for Image Recognition: Deep Residual Learning for Image Recognition
  
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin: Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
  
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism: GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
  
• Identity Mappings in Deep Residual Networks: Identity Mappings in Deep Residual Networks
  
• ImageNet classification with deep convolutional neural networks: ImageNet classification with deep convolutional neural networks
  
• Keeping Neural Networks Simple by Minimizing the Description Length of the Weights:
  
• Machine Super Intelligence:
  
• Multi-Scale Context Aggregation by Dilated Convolutions: Multi-Scale Context Aggregation by Dilated Convolutions
  
• Neural Machine Translation by Jointly Learning to Align and Translate: Neural Machine Translation by Jointly Learning to Align and Translate
  
• Neural Message Passing for Quantum Chemistry: Neural Message Passing for Quantum Chemistry
  
• Neural Turing Machines: Neural Turing Machines
  
• Order Matters: Sequence to sequence for sets: Order Matters: Sequence to sequence for sets
  
• Pointer Networks: Pointer Networks
  
• Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton: Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton
  
• Recurrent Neural Network Regularization: Recurrent Neural Network Regularization
  
• Relational recurrent neural networks: Relational recurrent neural networks
  
• Scaling Laws for Neural Language Models: Scaling Laws for Neural Language Models
  

• The Annotated Transformer:

• The First Law of Complexodynamics:

• The Unreasonable Effectiveness of Recurrent Neural Networks:

• Understanding LSTM Networks:

• Variational Lossy Autoencoder: Variational Lossy Autoencoder