| Do we actually need dense over-parameterization? in-time over-parameterization in sparse training S Liu, L Yin, DC Mocanu, M Pechenizkiy [ICML 2021] International Conference on Machine Learning, 6989-7000, 2021 | 136 | 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, ... [NeurIPS 2021] Advances in Neural Information Processing Systems 34, 9908-9922, 2021 | 132 | 2021 |
| 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, M Pechenizkiy, Y Liang, ... [ICML 2024] The Forty-first International Conference on Machine Learning, 2023 | 48 | 2023 |
| You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained Graph Tickets T Huang, T Chen, M Fang, V Menkovski, J Zhao, L Yin, Y Pei, DC Mocanu, ... [LoG BEST PAPER] Learning on Graphs Conference, LOG 2022, 2022 | 23* | 2022 |
| Dynamic Sparsity Is Channel-Level Sparsity Learner L Yin, G Li, M Fang, L Shen, T Huang, Z Wang, V Menkovski, X Ma, ... [NeurIPS 2023], 2023 | 20 | 2023 |
| Are Large Kernels Better Teachers than Transformers for ConvNets? T Huang, L Yin, Z Zhang, L Shen, M Fang, M Pechenizkiy, Z Wang, S Liu [ICML 2023] International Conference on Machine Learning, 2023 | 14 | 2023 |
| Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost L Yin, S Liu, M Fang, T Huang, V Menkovski, M Pechenizkiy [AAAI 2023] Thirty-Seventh AAAI Conference on Artificial Intelligence., 2022 | 12 | 2022 |
| From galore to welore: How low-rank weights non-uniformly emerge from low-rank gradients A Jaiswal, L Yin, Z Zhang, S Liu, J Zhao, Y Tian, Z Wang arXiv preprint arXiv:2407.11239, 2024 | 10 | 2024 |
| 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 | 10 | 2023 |
| Q-galore: Quantized galore with int4 projection and layer-adaptive low-rank gradients Z Zhang, A Jaiswal, L Yin, S Liu, J Zhao, Y Tian, Z Wang arXiv preprint arXiv:2407.08296, 2024 | 9 | 2024 |
| Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs L Yin, S Liu, A Jaiswal, S Kundu, Z Wang [ICML 2024] The Forty-first International Conference on Machine Learning, 2023 | 8* | 2023 |
| 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, ... [UAI 2022] The 38th Conference on Uncertainty in Artificial Intelligence, 2022 | 8 | 2022 |
| Knowledge Elicitation using Deep Metric Learning and Psychometric Testing L Yin, V Menkovski, M Pechenizkiy [ECML 2020] European Conference on Machine Learning, 2020 | 7 | 2020 |
| OwLore: Outlier-weighed Layerwise Sampled Low-Rank Projection for Memory-Efficient LLM Fine-tuning P Li, L Yin, X Gao, S Liu arXiv preprint arXiv:2405.18380, 2024 | 5 | 2024 |
| FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping A Jaiswal, B Hu, L Yin, Y Ro, S Liu, T Chen, A Akella [EMNLP 2024], 2024 | 5 | 2024 |
| Junk dna hypothesis: Pruning small pre-trained weights Irreversibly and Monotonically impairs “difficult” downstream tasks in llms,” L Yin, A Jaiswal, S Liu, S Kundu, Z Wang Forty-first International Conference on Machine Learning, 2024 | 4 | 2024 |
| A Structural-Clustering Based Active Learning for Graph Neural Networks RM Fajri, Y Pei, L Yin, M Pechenizkiy [IDA 2024] International Symposium on Intelligent Data Analysis, 2023 | 4 | 2023 |
| Enhancing Adversarial Training via Reweighting Optimization Trajectory T Huang, S Liu, T Chen, M Fang, L Shen, V Menkovski, L Yin, Y Pei, ... [ECML PKDD 2023] European Conference on Machine Learning and Principles and …, 2023 | 4 | 2023 |
| NeurRev: Train Better Sparse Neural Network Practically via Neuron Revitalization G Li, L Yin, J Ji, W Niu, M Qin, B Ren, L Guo, S Liu, X Ma [ICLR 2024] The Twelfth International Conference on Learning Representations, 2023 | 3 | 2023 |
| Hierarchical Semantic Segmentation using Psychometric Learning L Yin, V Menkovski, S Liu, M Pechenizkiy [ACML 2021 LONG ORAL] Asian Conference on Machine Learning, 2021 | 3 | 2021 |
Shiwei LiuUniversity of Oxford & Eindhoven University of TechnologyVerified email at maths.ox.ac.uk