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