Search Results for author: Josh C. Bongard

Found 6 papers, 3 papers with code

Combating catastrophic forgetting with developmental compression

no code implementations12 Apr 2018 Shawn L. E. Beaulieu, Sam Kriegman, Josh C. Bongard

Generally intelligent agents exhibit successful behavior across problems in several settings.

Interoceptive robustness through environment-mediated morphological development

1 code implementation6 Apr 2018 Sam Kriegman, Nick Cheney, Francesco Corucci, Josh C. Bongard

Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms.

Crowdsourcing Predictors of Residential Electric Energy Usage

no code implementations8 Sep 2017 Mark D. Wagy, Josh C. Bongard, James P. Bagrow, Paul D. H. Hines

In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy consumption.

Astronomy valid

Evolving Spatially Aggregated Features from Satellite Imagery for Regional Modeling

1 code implementation24 Jun 2017 Sam Kriegman, Marcin Szubert, Josh C. Bongard, Christian Skalka

Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight.

regression

A Minimal Developmental Model Can Increase Evolvability in Soft Robots

1 code implementation22 Jun 2017 Sam Kriegman, Nick Cheney, Francesco Corucci, Josh C. Bongard

Different subsystems of organisms adapt over many time scales, such as rapid changes in the nervous system (learning), slower morphological and neurological change over the lifetime of the organism (postnatal development), and change over many generations (evolution).

Nonlinear functional mapping of the human brain

no code implementations8 Sep 2015 Nicholas Allgaier, Tobias Banaschewski, Gareth Barker, Arun L. W. Bokde, Josh C. Bongard, Uli Bromberg, Christian Büchel, Anna Cattrell, Patricia J. Conrod, Christopher M. Danforth, Sylvane Desrivières, Peter S. Dodds, Herta Flor, Vincent Frouin, Jürgen Gallinat, Penny Gowland, Andreas Heinz, Bernd Ittermann, Scott Mackey, Jean-Luc Martinot, Kevin Murphy, Frauke Nees, Dimitri Papadopoulos-Orfanos, Luise Poustka, Michael N. Smolka, Henrik Walter, Robert Whelan, Gunter Schumann, Hugh Garavan, IMAGEN Consortium

In the present study, we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI from a 242-subject subset of the IMAGEN project, a European study of adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data.

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