Neural Random-Access Machines

Lie-Access Neural Turing Machines

External neural memory structures have recently become a popular tool for algorithmic deep learning (Graves et al., 2014; Weston et al., 2014). These models generally utilize differentiable versions of traditional discrete memory-access structures (random access, stacks, tapes) to provide the storage necessary for computational tasks. In this work, we argue that these neural memory systems lack...

THE "CAM-BRAIN" PROJECT The Evolutionary Engineering of a Billion Neuron Artificial Brain which Grows/Evolves at Electronic Speeds in a Cellular Automata Machine Part 1 : Fundamentals

This 2 part paper reports on the "CAM-Brain Project", which is an 8 year research project, beginning in 1993, at ATR labs in Kyoto, Japan, which intends to build/grow/evolve an artificial brain containing billions of artificial neurons. The essential idea behind CAM-Brain is to use cellular automata [Codd 1968] based neural networks which grow/evolve at electronic speeds inside cellular automat...

Performance evaluation of chain saw machines for dimensional stones using feasibility of neural network models

Prediction of the production rate of the cutting dimensional stone process is crucial, especially when chain saw machines are used. The cutting dimensional rock process is generally a complex issue with numerous effective factors including variable and unreliable conditions of the rocks and cutting machines. The Group Method of Data Handling (GMDH) type of neural network and Radial Basis Functi...

Fast Simulation of Turing Machines by Random Access Machines

The Linear Time Hierarchy Theorems for Abstract State Machines and RAMs

We prove the Linear Time Hierarchy Theorems for random access machines and Gurevich abstract state machines. One long-term goal of this line or research is to prove lower bounds for natural linear time problems.