A brain-inspired algorithm for training highly sparse neural networks
 Atashgahi, Z Pieterse, J Liu, S Mocanu, D Veldhuis, R Pechenizkiy, M Machine Learning volume 111 issue 12 4411-4452 (01 Dec 2022)
Hierarchical Semantic Segmentation using Psychometric Learning
 Yin, L Menkovski, V Liu, S Pechenizkiy, M Proceedings of Machine Learning Research volume 157 798-813 (01 Jan 2021)
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
 Liu, S Yin, L Mocanu, D Pechenizkiy, M Proceedings of Machine Learning Research volume 139 6989-7000 (01 Jan 2021)
Selfish Sparse RNN Training
 Liu, S Mocanu, D Pei, Y Pechenizkiy, M Proceedings of Machine Learning Research volume 139 6893-6904 (01 Jan 2021)
THE UNREASONABLE EFFECTIVENESS OF RANDOM PRUNING: RETURN OF THE MOST NAIVE BASELINE FOR SPARSE TRAINING
 Liu, S Chen, T Chen, X Shen, L Mocanu, D Wang, Z Pechenizkiy, M ICLR 2022 - 10th International Conference on Learning Representations (01 Jan 2022)
SPARSITY MAY CRY: LET US FAIL (CURRENT) SPARSE NEURAL NETWORKS TOGETHER!
 Liu, S Chen, T Zhang, Z Chen, X Huang, T Jaiswal, A Wang, Z 11th International Conference on Learning Representations, ICLR 2023 (01 Jan 2023)
Don’t Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
 Liu, S Tian, Y Chen, T Shen, L International Journal of Computer Vision volume 131 issue 10 2635-2648 (01 Oct 2023)
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
 Xiao, Q Wu, B Zhang, Y Liu, S Pechenizkiy, M Mocanu, E Mocanu, D Advances in Neural Information Processing Systems volume 35 (01 Jan 2022)
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
 Yin, L Menkovski, V Fang, M Huang, T Pei, Y Pechenizkiy, M Mocanu, D Liu, S Proceedings of Machine Learning Research volume 180 2267-2277 (01 Jan 2022)
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
 Huang, T Chen, T Fang, M Menkovski, V Zhao, J Yin, L Pei, Y Mocanu, D Wang, Z Pechenizkiy, M Liu, S Proceedings of Machine Learning Research volume 198 (01 Jan 2022)
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