Notable Papers:

(there is a lot here, I will try to organize as I am familar with all papers listed below)

• A Logical Calculus of the Ideas Immanent in Nervous Activity

• The Logic Theory Machine; A Complex Information Processing System

• The Logic Theory Machine: A Model Heuristic Program

• Learning representations by back-propogating errors

• Computing Machinery and Intelligence

• A Threshold Selection Method from Gray-Level Histograms

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

• Deep Residual Learning for Image Recognition

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting

• Induction of Decision Trees

• Large-scale Video Classification with Convolutional Neural Networks

• Efficient Object Localization Using Convolutional Networks

• WaveNet: A Generative Model for Raw Audio

• A guide to convolution arithmetic for deep learning

• Deconvolutional Networks

• Long Short Term Memory

• Supervised Sequence Labelling with Recurrent Neural Networks

• A Theoretically Grounded Application of Dropout in Recurrent Neural Networks

• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

• On the Properties of Neural Machine Translation: Encoder-Decoder Approaches

• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

• Zero-bias autoencoders and the benefits of co-adapting features

• Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

• Rectifier Nonlinearities Improve Neural Network Acoustic Models

• Self-Normalizing Neural Networks

• Efficient Estimation of Word Representations in Vector Space

• On the importance of initialization and momentum in deep learning

• Incorporating Nesterov Momentum into Adam

• On the Convergence of Adam and Beyond

• Adam: A Method for Stochastic Optimization

• ADADELTA: An Adaptive Learning Rate Method

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

• Identity Mappings in Deep Residual Networks

• Aggregated Residual Transformations for Deep Neural Networks

• Very Deep Convolutional Networks for Large-Scale Image Recognition

• Xception: Deep Learning with Depthwise Separable Convolutions

• Learning Transferable Architectures for Scalable Image Recognition

• Densely Connected Convolutional Networks

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

• Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

• Rethinking the Inception Architecture for Computer Vision

• MobileNetV2: Inverted Residuals and Linear Bottlenecks

• Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

• Understanding the difficulty of training deep feedforward neural networks

• Efficient BackProp

• Exact solutions to the nonlinear dynamics of learning in deep linear neural networks

• Learning to Execute

• Sequence to Sequence Learning with Neural Networks

• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

• End-To-End Memory Networks

• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

• How transferable are features in deep neural networks?

• Neural Machine Translation by Jointly Learning to Align and Translate

• Effective Approaches to Attention-based Neural Machine Translation

Notable Figures:

• Norbert Wiener

• Marvin Minsky

• Richard Wallace

cybernetics

https://medium.com/@jankrikkeChina/cybernetics-explains-what-ai-is-and-what-it-isnt-13b2baec6cca