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Shiwei Liu
Shiwei Liu
University of Oxford & Eindhoven University of Technology
Verified email at maths.ox.ac.uk - Homepage
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Year
More convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity
S Liu, T Chen, X Chen, X Chen, Q Xiao, B Wu, M Pechenizkiy, D Mocanu, ...
ICLR2023, The International Conference on Learning Representations, 2023
1782023
Do we actually need dense over-parameterization? in-time over-parameterization in sparse training
S Liu, L Yin, DC Mocanu, M Pechenizkiy
ICML2021, International Conference on Machine Learning, 2021
1322021
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
1272021
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
ICLR2022, The International Conference on Learning Representations, 2022
1132022
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, 2589-2604, 2021
972021
Selfish sparse RNN training
S Liu, DC Mocanu, Y Pei, M Pechenizkiy
ICML2021, International Conference on Machine Learning, 2021
89*2021
Adamerging: Adaptive model merging for multi-task learning
E Yang, Z Wang, L Shen, S Liu, G Guo, X Wang, D Tao
ICLR2024, The International Conference on Learning Representations, 2024
572024
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
572021
Outlier weighed layerwise sparsity (owl): A missing secret sauce for pruning llms to high sparsity
L Yin, Y Wu, Z Zhang, CY Hsieh, Y Wang, Y Jia, G Li, A Jaiswal, ...
ICML2024, International Conference on Machine Learning, 2024
422024
Topological Insights into Sparse Neural Networks
S Liu, T Van der Lee, A Yaman, Z Atashgahi, D Ferraro, G Sokar, ...
ECML2020, European Conference on Machine Learning, 2020
352020
Dynamic sparse no training: Training-free fine-tuning for sparse llms
Y Zhang, L Zhao, M Lin, Y Sun, Y Yao, X Han, J Tanner, S Liu, R Ji
ICLR2024, The International Conference on Learning Representations, 2024
342024
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
A Jaiswal, S Liu, T Chen, Z Wang
NeurIPS2023, 37th Annual Conference on Neural Information Processing Systems, 2023
322023
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers
T Chen, Z Zhang, A Jaiswal, S Liu, Z Wang
ICLR2023, The International Conference on Learning Representations, 2023
312023
Revisiting pruning at initialization through the lens of Ramanujan graph
DNM Hoang, S Liu, R Marculescu, Z Wang
ICLR2023, The International Conference on Learning Representations, 2023
302023
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
S Liu, T Chen, Z Zhang, X Chen, T Huang, A Jaiswal, Z Wang
ICLR2023, The International Conference on Learning Representations, 2023
292023
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
292022
A Brain-inspired Algorithm for Training Highly Sparse Neural Networks
Z Atashgahi, J Pieterse, S Liu, DC Mocanu, R Veldhuis, M Pechenizkiy
Machine Learning Journal (ECML-PKDD 2022 journal track), 2019
29*2019
Dynamic Sparse Network for Time Series Classification: Learning What to “See”
Q Xiao, B Wu, Y Zhang, S Liu, M Pechenizkiy, E Mocanu, DC Mocanu
NeurIPS2022, 36th Annual Conference on Neural Information Processing Systems, 2022
282022
Visual prompting upgrades neural network sparsification: A data-model perspective
C Jin, T Huang, Y Zhang, M Pechenizkiy, S Liu, S Liu, T Chen
arXiv preprint arXiv:2312.01397, 2023
252023
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
T Huang, T Chen, M Fang, V Menkovski, J Zhao, L Yin, Y Pei, DC Mocanu, ...
LoG 2022, Learning on Graphs Conference (Best Paper Award), 2022
212022
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