Fast Simulation of Turing Machines by Random Access Machines

Fast Simulation of Turing Machines by 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...

Small fast universal Turing machines

We present a number of time-efficient small universal Turing machines. We show that there exists deterministic polynomial time universal Turing machines with state-symbol products of (3, 11), (5, 7), (6, 6), (7, 5) and (8, 4). These machines are the smallest known universal Turing machines that simulate TMs in polynomial time.

Random-Access Machines and the Extended Church-Turing Thesis

We show that a random-access machine (RAM) can simulate any effective algorithm with only constant overhead of time, thereby supporting the Extended Church-Turing Thesis.

Some improvements in fuzzy turing machines

In this paper, we improve some previous definitions of fuzzy-type Turing machines to obtain degrees of accepting and rejecting in a computational manner. We apply a BFS-based search method and some level’s upper bounds to propose a computational process in calculating degrees of accepting and rejecting. Next, we introduce the class of Extended Fuzzy Turing Machines equipped with indeterminacy s...