Tamas Madl
Tamas Madl
University of Manchester; Austrian Institute for Artificial Intelligence
Verified email at
Cited by
Cited by
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
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
DigitCult-Scientific Journal on Digital Cultures 2 (2), 9-24, 2017
A LIDA-based model of the attentional blink
T Madl, S Franklin
ICCM 2012 proceedings 283, 2012
Network analysis of heart beat intervals using horizontal visibility graphs
T Madl
Computing in Cardiology, 2016
Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture
T Madl, S Franklin
A Construction Manual for Robots' Ethical Systems 1, 2015
Exploring the structure of spatial representations
T Madl, S Franklin, K Chen, R Trappl, D Montaldi
PloS one 11 (6), e0157343, 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
Safe Semi-Supervised Learning of Sum-Product Networks
M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl
Uncertainty in Artificial Intelligence, 2017
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
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
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
T Madl, W Xu, O Choudhury, M Howard
AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI), 2022
The Timing of the Cognitive Cycle
M Tamas, B Bernard, F Stan
PLos ONE, 4, 2011
Automated Disease Classification Using Whole Genome Sequencing (WGS) and Whole Transcriptome Sequencing (WTS) Data with Transparent Artificial Intelligence (AI)
N Nadarajah, EP Coyotl, J Golden, S Hutter, T Madl, M Meggendorfer, ...
Blood 138, 275, 2021
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