| 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 33 (7), 2589-2604, 2021 | 46 | 2021 |
| Do we actually need dense over-parameterization? in-time over-parameterization in sparse training S Liu, L Yin, DC Mocanu, M Pechenizkiy International Conference on Machine Learning (ICML 2021), 2021 | 20 | 2021 |
| Efficient and effective training of sparse recurrent neural networks S Liu, I Ni’mah, V Menkovski, DC Mocanu, M Pechenizkiy Neural Computing and Applications, 1-12, 2021 | 19* | 2021 |
| Selfish sparse RNN training S Liu, DC Mocanu, Y Pei, M Pechenizkiy International Conference on Machine Learning (ICML 2021), 2021 | 17 | 2021 |
| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... Advances in Neural Information Processing Systems (NeurIPS 2021), 2021 | 15 | 2021 |
| Topological Insights into Sparse Neural Networks S Liu, T Van der Lee, A Yaman, Z Atashgahi, D Ferraro, G Sokar, ... European Conference on Machine Learning (ECML 2020), 2020 | 14* | 2020 |
| A Brain-inspired Algorithm for Training Highly Sparse Neural Networks Z Atashgahi, J Pieterse, S Liu, DC Mocanu, R Veldhuis, M Pechenizkiy arXiv preprint arXiv:1903.07138, 2019 | 7* | 2019 |
| 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, ... The International Conference on Learning Representations (ICLR 2022), 2021 | 6* | 2021 |
| Learning Sparse Neural Networks for Better Generalization S Liu International Joint Conference on Artificial Intelligence (IJCAI), 2020 | 6 | 2020 |
| On improving deep learning generalization with adaptive sparse connectivity S Liu, DC Mocanu, M Pechenizkiy ICML 2019 workshop of Understanding and Improving Generalization in Deep …, 2019 | 4 | 2019 |
| 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 The International Conference on Learning Representations (ICLR 2022), 2022 | 3 | 2022 |
| Achieving Personalized Federated Learning with Sparse Local Models T Huang, S Liu, L Shen, F He, W Lin, D Tao arXiv preprint arXiv:2201.11380, 2022 | 2 | 2022 |
| Hierarchical semantic segmentation using psychometric learning L Yin, V Menkovski, S Liu, M Pechenizkiy arXiv preprint arXiv:2107.03212, 2021 | 2 | 2021 |
| Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance S Liu, Y Tian, T Chen, L Shen arXiv preprint arXiv:2203.02770, 2022 | 1 | 2022 |
| Sparse Neural Network Training with In-Time Over-Parameterization S Liu PhD thesis, 2022 | | 2022 |
| Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training L Yin, V Menkovski, M Fang, T Huang, Y Pei, M Pechenizkiy, DC Mocanu, ... The 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022), 2022 | | 2022 |
| Sparse Unbalanced GAN Training with In-Time Over-Parameterization S Liu, Y Tian, T Chen, L Shen | | 2021 |
| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration (Poster) S Liu, T Chen, X Chen, Z Atashgahi, L Yin, H Kou, L Shen, M Pechenizkiy, ... Sparsity in Neural Networks: Advancing Understanding and Practice 2021, 2021 | | 2021 |
| Network Performance Optimization with Real Time Traffic Prediction in Data Center Network F Yan, S Liu, N Calabretta 2020 European Conference on Optical Communications (ECOC), 1-4, 2020 | | 2020 |
Decebal Constantin MocanuAssistant Professor at University of Twente, Eindhoven University of TechnologyVerified email at utwente.nl
