Decebal Constantin Mocanu
Decebal Constantin Mocanu
Associate Professor in Machine Learning, University of Luxembourg, TU Eindhoven
Verified email at - Homepage
Cited by
Cited by
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
DC Mocanu, E Mocanu, P Stone, PH Nguyen, M Gibescu, A Liotta
Nature communications (19 June 2018), arXiv:1707.04780, 2017
On-line building energy optimization using deep reinforcement learning
E Mocanu, DC Mocanu, PH Nguyen, A Liotta, ME Webber, M Gibescu, ...
IEEE transactions on smart grid 10 (4), 3698-3708, 2018
A topological insight into restricted Boltzmann machines
DC Mocanu, E Mocanu, PH Nguyen, M Gibescu, A Liotta
Machine Learning 104 (2), 243--270, 2016
An automated measure of mdp similarity for transfer in reinforcement learning
HB Ammar, E Eaton, ME Taylor, DC Mocanu, K Driessens, G Weiss, ...
Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014
Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance
F Cauteruccio, G Fortino, A Guerrieri, A Liotta, DC Mocanu, C Perra, ...
Information Fusion 52, 13-30, 2019
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ...
NeurIPS 2021, Thirty-fifth Conference on Neural Information Processing …, 2021
Do we actually need dense over-parameterization? in-time over-parameterization in sparse training
S Liu, L Yin, DC Mocanu, M Pechenizkiy
ICML 2021, Thirty-eighth International Conference on Machine Learning, 2021
More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity
S Liu, T Chen, X Chen, X Chen, Q Xiao, B Wu, T Kärkkäinen, ...
ICLR 2023, The Eleventh International Conference on Learning Representations …, 2023
Sparse evolutionary deep learning with over one million artificial neurons on commodity hardware
S Liu, DC Mocanu, ARR Matavalam, Y Pei, M Pechenizkiy
Neural Computing and Applications (6 July 2020) 33 (7), 2589-2604, 2019
Deep learning for quality assessment in live video streaming
MT Vega, DC Mocanu, J Famaey, S Stavrou, A Liotta
IEEE signal processing letters 24 (6), 736-740, 2017
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
S Liu, T Chen, X Chen, L Shen, DC Mocanu, Z Wang, M Pechenizkiy
ICLR 2022, The Tenth International Conference on Learning Representations …, 2022
SpaceNet: Make Free Space For Continual Learning
G Sokar, DC Mocanu, M Pechenizkiy
Neurocomputing 439 (7 June 2021), 1-11, 2020
Automatically mapped transfer between reinforcement learning tasks via three-way restricted boltzmann machines
HB Ammar, DC Mocanu, ME Taylor, K Driessens, K Tuyls, G Weiss
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013
Big IoT data mining for real-time energy disaggregation in buildings
DC Mocanu, E Mocanu, PH Nguyen, M Gibescu, A Liotta
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC …, 2016
Factored four way conditional restricted boltzmann machines for activity recognition
DC Mocanu, HB Ammar, D Lowet, K Driessens, A Liotta, G Weiss, K Tuyls
Pattern Recognition Letters 66, 100-108, 2015
Towards ABAC Policy Mining from Logs with Deep Learning
DC Mocanu, F Turkmen, A Liotta
18th International Multiconference, IS 2015, Intelligent Systems, Ljubljana …, 2015
Factored Four Way Conditional Restricted Boltzmann Machines for Activity Recognition (Type B Paper)
DC Mocanu, HB Ammar, D Lowet, K Driessens, A Liotta, G Weiss, K Tuyls
Proceedings of the 27th Benelux Conference on Artificial Intelligence …, 2015
Predictive no-reference assessment of video quality
MT Vega, DC Mocanu, S Stavrou, A Liotta
Signal Processing: Image Communication 52, 20-32, 2017
DC Mocanu, G Exarchakos, A Liotta
IEEE International Conference on Image Processing (ICIP), 2014
When does lower bitrate give higher quality in modern video services?
DC Mocanu, A Liotta, A Ricci, MT Vega, G Exarchakos
2014 IEEE Network Operations and Management Symposium (NOMS), 1-5, 2014
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