Jürgen Schmidhuber has written a fair amount of cool stuff.

He has a tendency to use many acronyms in his papers. This post is an attempt to summarize some of them.

Papers:

General

  • AI Artificial Intelligence
  • AGI Artificial General Intelligence
  • DL Deep Learning
  • RL Reinforcement Learning
  • SL Supervised Learning
  • UL Unsupervised Learning

Networks

  • NN Neural Network
  • ANN Artificial Neural Network
  • BNN Biological Neural Network
  • CNN Convolutional Neural Network
  • FNN Feedforward Neural Network
  • RNN Recurrent Neural Network
  • BRNN Bi-directional Recurrent Neural Network
  • LSTM Long Short Term Memory
  • BP Backpropagation
  • BPTT BackPropagation Through Time

Other

  • AE AutoEncoder
  • AIT Algorithmic information theory
  • BM Boltzmann Machine
  • CAP Credit Assignment Path
  • CEC Constant Error Carousel
  • CFL Context Free Language
  • CM Controller-Model system
  • CMA-ES Covariance Matrix Estimation Evolution Strategies
  • CoSyNE Co-Synaptic Neuro-Evolution
  • CSL Context Senistive Language
  • CTC Connectionist Temporal Classification (gradient-based method to train RNNs) paper
  • DCT Discrete Cosine Transform
  • DBN Deep Belief Network
  • DP Dynamic Programming
  • DS Direct Policy Search
  • EA Evolutionary Algorithm
  • EM Expectation Maximization
  • FMS Flat Minimum Search
  • FSA Finite State Automaton
  • GMDH Group Method of Data Handling
  • GOFAI Good Old-Fashioned Artificial Intelligence
  • GP Genetic Programming
  • GPU Graphics Processing Unit
  • GP MPCNN: GPU-Based Max-Pooling Convolutional Neural Network
  • HMM Hidden Markov Model
  • HRL Hierarchical Reinforcement Learning
  • HTM Hierarchical Temporal Memory
  • HMAX Hierarchical Model “and X”
  • HRL NN-based Hierarchical RL
  • MC Multi-Column
  • MCTS Monte Carlo Tree Sampling (also Monte Carlo Tree Search)
  • MDL Minimum Description Length
  • MDP Markov Decision Process
  • MNIST Mixed National Institute of Standards and Technology Database
  • MP Max-Pooling
  • MPCNN Max-Pooling Convolutional Neural Network
  • NEAT NeuroEvolution of Augmenting Topologies
  • NE NeuroEvolution, (using evolutionary computation to train artifi-cial neural networks) paper
  • NES Natural Evolution Strategies
  • NFQ Neural Fitted Q-Learning paper
  • PCC Potential Causal Connection
  • PDCC Potential Direct Causal Connection
  • PG Policy Gradient
  • PM Predictability Minimization
  • POMDP Partially Observable Markov Decision Process
  • RAAM Recursive Auto-Associative Memory
  • RBM Restricted Boltzmann Machine
  • ReLU Rectified Linear Unit
  • R-prop: Resilient Backpropagation
  • RNNAI RNN-based AI
  • SLIM NN: Self-Delimiting Neural Network
  • SOTA Self-Organising Tree Algorithm
  • SVM Support Vector Machine
  • TDNN Time-Delay Neural Network
  • TIMIT TI/SRI/MIT Acoustic-Phonetic Continuous Speech Corpus
  • WTA Winner-Take-All
  • TORCS The Open Racing Car Simulator wiki