The Annotated Transformer

The First Law of Complexodynamics

The Unreasonable Effectiveness of RNNs

Understanding LSTM Networks

Recurrent Neural Network Regularization

Keeping Neural Networks Simple by Minimizing the Description Length of the Weights

Pointer Networks

ImageNet Classification with Deep CNNs

Order Matters: Sequence to sequence for sets

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

Deep Residual Learning for Image Recognition

Multi-Scale Context Aggregation by Dilated Convolutions

Neural 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 RNNs

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 LMs

A Tutorial Introduction to the Minimum Description Length Principle

Machine Super Intelligence Dissertation

PAGE 434 onwards: Komogrov Complexity

CS231n Convolutional Neural Networks for Visual Recognition