Search Results for author: J. P. Crutchfield

Found 6 papers, 1 papers with code

Thermodynamic Machine Learning through Maximum Work Production

no code implementations27 Jun 2020 A. B. Boyd, J. P. Crutchfield, M. Gu

We introduce the thermodynamic principle that work production is the most relevant performance metric for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning.

BIG-bench Machine Learning

Inference, Prediction, and Entropy-Rate Estimation of Continuous-time, Discrete-event Processes

no code implementations7 May 2020 S. E. Marzen, J. P. Crutchfield

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground.

Probabilistic Deterministic Finite Automata and Recurrent Networks, Revisited

no code implementations17 Oct 2019 S. E. Marzen, J. P. Crutchfield

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice.

Optimizing Quantum Models of Classical Channels: The reverse Holevo problem

no code implementations23 Sep 2017 S. Loomis, J. R. Mahoney, C. Aghamohammadi, J. P. Crutchfield

Given a classical channel---a stochastic map from inputs to outputs---the input can often be transformed to an intermediate variable that is informationally smaller than the input.

Identifying Functional Thermodynamics in Autonomous Maxwellian Ratchets

1 code implementation6 Jul 2015 A. B. Boyd, D. Mandal, J. P. Crutchfield

We introduce a family of Maxwellian Demons for which correlations among information bearing degrees of freedom can be calculated exactly and in compact analytical form.

Statistical Mechanics Dynamical Systems Chaotic Dynamics Biological Physics Chemical Physics

Pairwise Correlations in Layered Close-Packed Structures

no code implementations26 Jul 2014 P. M. Riechers, D. P. Varn, J. P. Crutchfield

Given a description of the stacking statistics of layered close-packed structures in the form of a hidden Markov model, we develop analytical expressions for the pairwise correlation functions between the layers.

Cannot find the paper you are looking for? You can Submit a new open access paper.