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Tamas Madl
Tamas Madl
University of Manchester; Austrian Institute for Artificial Intelligence
Verified email at postgrad.manchester.ac.uk
Title
Cited by
Cited by
Year
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
2782013
The timing of the cognitive cycle
T Madl, BJ Baars, S Franklin
PloS one 6 (4), e14803, 2011
1522011
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
1142015
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
892016
Bayesian Integration of Information in Hippocampal Place Cells
T Madl, S Franklin, K Chen, D Montaldi, R Trappl
PLoS ONE, e89762, 2014
382014
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
242016
Spatial Working Memory in the LIDA Cognitive Architecture
T Madl, S Franklin, K Chen, R Trappl
ICCM 2013, 2013
232013
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
222017
A LIDA-based model of the attentional blink
T Madl, S Franklin
222012
Network analysis of heart beat intervals using horizontal visibility graphs
T Madl
Computing in Cardiology, 2016
212016
Exploring the structure of spatial representations
T Madl, S Franklin, K Chen, R Trappl, D Montaldi
PloS one 11 (6), e0157343, 2016
202016
Constrained Incrementalist Moral Decision Making for a Biologically Inspired Cognitive Architecture
T Madl, S Franklin
A Construction Manual for Robots' Ethical Systems 1, 2015
202015
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
182018
Safe Semi-Supervised Learning of Sum-Product Networks
M Trapp, T Madl, R Peharz, F Pernkopf, R Trappl
Uncertainty in Artificial Intelligence, 2017
172017
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
162016
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
102016
Deep neural heart rate variability analysis
T Madl
NIPS 2016 Workshop on Machine Learning for Health (ML4HC), 2016
62016
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
42022
The Timing of the Cognitive Cycle
M Tamas, B Bernard, F Stan
PLos ONE, 4, 2011
22011
Explaining multiclass classifiers with categorical values: A case study in radiography
L Franceschi, C Zor, MB Zafar, G Detommaso, C Archambeau, T Madl, ...
International Workshop on Trustworthy Machine Learning for Healthcare, 11-24, 2023
12023
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