| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... NeurIPS2021, Advances in Neural Information Processing Systems, 2021 | 101 | 2021 |
| Deep ensembling with no overhead for either training or testing: The all-round blessings of dynamic sparsity S Liu, T Chen, Z Atashgahi, X Chen, G Sokar, E Mocanu, M Pechenizkiy, ... ICLR2022, The International Conference on Learning Representations, 2021 | 44 | 2021 |
| Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders Z Atashgahi, G Sokar, T van der Lee, E Mocanu, DC Mocanu, R Veldhuis, ... Machine Learning Journal (ECML-PKDD 2022 journal track), 2021 | 32 | 2021 |
| Topological insights into sparse neural networks S Liu, T Van der Lee, A Yaman, Z Atashgahi, D Ferraro, G Sokar, ... ECML-PKDD2020, European Conference on Machine Learning and Principles and …, 2020 | 32 | 2020 |
| A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Z Atashgahi, J Pieterse, S Liu, D Constantin Mocanu, R Veldhuis, ... Machine Learning Journal (ECML-PKDD 2022 journal track), 2022 | 21* | 2022 |
| Where to Pay Attention in Sparse Training for Feature Selection? G Sokar, Z Atashgahi, M Pechenizkiy, DC Mocanu NeurIPS2022, 36th Annual Conference on Neural Information Processing Systems, 2022 | 12 | 2022 |
| Memory-free Online Change-point Detection: A Novel Neural Network Approach Z Atashgahi, DC Mocanu, R Veldhuis, M Pechenizkiy arXiv preprint arXiv:2207.03932, 2022 | 7 | 2022 |
| Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks Z Atashgahi, X Zhang, N Kichler, S Liu, L Yin, M Pechenizkiy, R Veldhuis, ... TMLR, Transactions on Machine Learning Research, 2023 | 6 | 2023 |
| Abnormal Activity Detection for the Elderly People using ConvLSTM Autoencoder E Nazerfard, Z Atashgahi, A Nadali | 4 | 2021 |
| Adaptive sparsity level during training for efficient time series forecasting with transformers Z Atashgahi, M Pechenizkiy, R Veldhuis, DC Mocanu arXiv preprint arXiv:2305.18382, 2023 | 1 | 2023 |
| Unsupervised online memory-free change-point detection using an ensemble of LSTM-autoencoder-based neural networks Z Atashgahi, DC Mocanu, R Veldhuis, M Pechenizkiy 8th ACM Celebration of Women in Computing womENcourage, 2021 | 1 | 2021 |
| Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks K Liu, Z Atashgahi, G Sokar, M Pechenizkiy, DC Mocanu International Conference on Artificial Intelligence and Statistics, 3952-3960, 2024 | | 2024 |
| Advancing Efficiency in Neural Networks through Sparsity and Feature Selection Z Atashgahi | | 2024 |
| FASTR Feature selection using A Sparse Training Regime MD Keep, M Pechenizkiy, DC Mocanu, Z Atashgahi, G Sokar | | 2023 |
| Cost-effective Artificial Neural Networks Z Atashgahi IJCAI-23, Doctoral Consortium Track, 2023 | | 2023 |
| Robustness of sparse MLPs for supervised feature selection (poster) N Kichler, Z Atashgahi, DC Mocanu Sparsity in Neural Networks: Advancing Understanding and Practice 2021, 2021 | | 2021 |
| Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders (poster) Z Atashgahi, GAZN Sokar, T van der Lee, E Mocanu, DC Mocanu, ... Sparsity in Neural Networks: Advancing Understanding and Practice 2021, 2021 | | 2021 |
Decebal Constantin MocanuAssociate Professor in Machine Learning, University of Luxembourg, TU EindhovenDirección de correo verificada de uni.lu
