Tamas Madl
Tamas Madl
University of Manchester; Austrian Institute for Artificial Intelligence
Adresse e-mail validée de postgrad.manchester.ac.uk
Citée par
Citée par
LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning
S Franklin, T Madl, S D’Mello, J Snaider
IEEE Transactions on Autonomous Mental Development, 1, 2013
The timing of the cognitive cycle
T Madl, BJ Baars, S Franklin
PloS one 6 (4), e14803, 2011
Computational cognitive models of spatial memory in navigation space: A review
T Madl, K Chen, D Montaldi, R Trappl
Neural Networks 65, 18-43, 2015
A LIDA cognitive model tutorial
S Franklin, T Madl, S Strain, U Faghihi, D Dong, S Kugele, J Snaider, ...
Biologically Inspired Cognitive Architectures 16, 105-130, 2016
Bayesian Integration of Information in Hippocampal Place Cells
T Madl, S Franklin, K Chen, D Montaldi, R Trappl
PLoS ONE, e89762, 2014
A LIDA-based model of the attentional blink
T Madl, S Franklin
ICCM 2012 proceedings, 283-288, 2012
Towards real-world capable spatial memory in the LIDA cognitive architecture
RT Tamas Madl, Stan Franklin, Ke Chen, Daniela Montaldi
Biologically Inspired Cognitive Architectures, 2016
Spatial Working Memory in the LIDA Cognitive Architecture
T Madl, S Franklin, K Chen, R Trappl
ICCM 2013, 2013
Deep machine learning application to the detection of preclinical neurodegenerative diseases of aging
MJ Summers, T Madl, AE Vercelli, G Aumayr, DM Bleier, L Ciferri
DigiCult: Scientific Journal on Digital Cultures 2 (2), 9-24, 2017
Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture
T Madl, S Franklin
A Construction Manual for Robots' Ethical Systems 1, 2015
Network analysis of heart beat intervals using horizontal visibility graphs
T Madl
Computing in Cardiology, 2016
Safe Semi-Supervised Learning of Sum-Product Networks
M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl
Uncertainty in Artificial Intelligence, 2017
Exploring the structure of spatial representations
T Madl, S Franklin, K Chen, R Trappl, D Montaldi
PloS one 11 (6), e0157343, 2016
Structure inference in sum-product networks using infinite sum-product trees
M Trapp, R Peharz, M Skowron, T Madl, F Pernkopf, R Trappl
NIPS Workshop on Practical Bayesian Nonparametrics, 2016
A computational cognitive framework of spatial memory in brains and robots
T Madl, S Franklin, K Chen, R Trappl
Cognitive Systems Research 47, 147-172, 2018
Continuity and the Flow of Time - A Cognitive Science Perspective
T Madl, S Franklin, J Snaider, U Faghihi
Philosophy and Psychology of Time 1, 2016
Deep neural heart rate variability analysis
T Madl
NIPS 2016 Workshop on Machine Learning for Health (ML4HC), 2016
Bayesian mechanisms in spatial cognition: Towards real-world capable computational cognitive models of spatial memory
T Madl
The University of Manchester, Manchester, UK, 2016
Smartphone-based paroxysmal atrial fibrillation monitoring with robust generalization
T Madl, D Madl
arXiv preprint arXiv:1711.10862, 2017
Deep machine learning detection of preclinical neurodegenerative diseases
L Ciferri, MJ Summers, T Madl, A Vercelli, G Aumayr, GL Colombo
World Health Organization, 2017
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