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
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
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
Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture
T Madl, S Franklin
A Construction Manual for Robots' Ethical Systems 1, 2015
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
Exploring the structure of spatial representations
T Madl, S Franklin, K Chen, R Trappl, D Montaldi
PloS one 11 (6), e0157343, 2016
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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