41,874 research outputs found
Structural Drift: The Population Dynamics of Sequential Learning
We introduce a theory of sequential causal inference in which learners in a
chain estimate a structural model from their upstream teacher and then pass
samples from the model to their downstream student. It extends the population
dynamics of genetic drift, recasting Kimura's selectively neutral theory as a
special case of a generalized drift process using structured populations with
memory. We examine the diffusion and fixation properties of several drift
processes and propose applications to learning, inference, and evolution. We
also demonstrate how the organization of drift process space controls fidelity,
facilitates innovations, and leads to information loss in sequential learning
with and without memory.Comment: 15 pages, 9 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/sdrift.ht
Are there new models of computation? Reply to Wegner and Eberbach
Wegner and Eberbach[Weg04b] have argued that there are fundamental limitations
to Turing Machines as a foundation of computability and that these can be overcome
by so-called superTuring models such as interaction machines, the [pi]calculus and the
$-calculus. In this paper we contest Weger and Eberbach claims
Counter Machines and Distributed Automata: A Story about Exchanging Space and Time
We prove the equivalence of two classes of counter machines and one class of
distributed automata. Our counter machines operate on finite words, which they
read from left to right while incrementing or decrementing a fixed number of
counters. The two classes differ in the extra features they offer: one allows
to copy counter values, whereas the other allows to compute copyless sums of
counters. Our distributed automata, on the other hand, operate on directed path
graphs that represent words. All nodes of a path synchronously execute the same
finite-state machine, whose state diagram must be acyclic except for
self-loops, and each node receives as input the state of its direct
predecessor. These devices form a subclass of linear-time one-way cellular
automata.Comment: 15 pages (+ 13 pages of appendices), 5 figures; To appear in the
proceedings of AUTOMATA 2018
A Modular Formalization of Reversibility for Concurrent Models and Languages
Causal-consistent reversibility is the reference notion of reversibility for
concurrency. We introduce a modular framework for defining causal-consistent
reversible extensions of concurrent models and languages. We show how our
framework can be used to define reversible extensions of formalisms as
different as CCS and concurrent X-machines. The generality of the approach
allows for the reuse of theories and techniques in different settings.Comment: In Proceedings ICE 2016, arXiv:1608.0313
Reversible Logic Elements with Memory and Their Universality
Reversible computing is a paradigm of computation that reflects physical
reversibility, one of the fundamental microscopic laws of Nature. In this
survey, we discuss topics on reversible logic elements with memory (RLEM),
which can be used to build reversible computing systems, and their
universality. An RLEM is called universal, if any reversible sequential machine
(RSM) can be realized as a circuit composed only of it. Since a finite-state
control and a tape cell of a reversible Turing machine (RTM) are formalized as
RSMs, any RTM can be constructed from a universal RLEM. Here, we investigate
2-state RLEMs, and show that infinitely many kinds of non-degenerate RLEMs are
all universal besides only four exceptions. Non-universality of these
exceptional RLEMs is also argued.Comment: In Proceedings MCU 2013, arXiv:1309.104
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
Inspired by number series tests to measure human intelligence, we suggest
number sequence prediction tasks to assess neural network models' computational
powers for solving algorithmic problems. We define the complexity and
difficulty of a number sequence prediction task with the structure of the
smallest automaton that can generate the sequence. We suggest two types of
number sequence prediction problems: the number-level and the digit-level
problems. The number-level problems format sequences as 2-dimensional grids of
digits and the digit-level problems provide a single digit input per a time
step. The complexity of a number-level sequence prediction can be defined with
the depth of an equivalent combinatorial logic, and the complexity of a
digit-level sequence prediction can be defined with an equivalent state
automaton for the generation rule. Experiments with number-level sequences
suggest that CNN models are capable of learning the compound operations of
sequence generation rules, but the depths of the compound operations are
limited. For the digit-level problems, simple GRU and LSTM models can solve
some problems with the complexity of finite state automata. Memory augmented
models such as Stack-RNN, Attention, and Neural Turing Machines can solve the
reverse-order task which has the complexity of simple pushdown automaton.
However, all of above cannot solve general Fibonacci, Arithmetic or Geometric
sequence generation problems that represent the complexity of queue automata or
Turing machines. The results show that our number sequence prediction problems
effectively evaluate machine learning models' computational capabilities.Comment: Accepted to 2019 AAAI Conference on Artificial Intelligenc
Descriptive Complexity of Deterministic Polylogarithmic Time and Space
We propose logical characterizations of problems solvable in deterministic
polylogarithmic time (PolylogTime) and polylogarithmic space (PolylogSpace). We
introduce a novel two-sorted logic that separates the elements of the input
domain from the bit positions needed to address these elements. We prove that
the inflationary and partial fixed point vartiants of this logic capture
PolylogTime and PolylogSpace, respectively. In the course of proving that our
logic indeed captures PolylogTime on finite ordered structures, we introduce a
variant of random-access Turing machines that can access the relations and
functions of a structure directly. We investigate whether an explicit predicate
for the ordering of the domain is needed in our PolylogTime logic. Finally, we
present the open problem of finding an exact characterization of
order-invariant queries in PolylogTime.Comment: Submitted to the Journal of Computer and System Science
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