Sheng Liu
Cited by
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Early-learning regularization prevents memorization of noisy labels
S Liu, J Niles-Weed, N Razavian, C Fernandez-Granda
34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020
On the design of convolutional neural networks for automatic detection of Alzheimer’s disease
S Liu, C Yadav, C Fernandez-Granda, N Razavian
2019 NeurIPS, 184-201, 2020
Adaptive early-learning correction for segmentation from noisy annotations
S Liu, K Liu, W Zhu, Y Shen, C Fernandez-Granda
CVPR 2022 (Oral), 2606-2616, 2022
Robust Training under Label Noise by Over-parameterization
S Liu, Z Zhu, Q Qu, C You
ICML 2022, 2022
On Learning Contrastive Representations for Learning with Noisy Labels
L Yi, S Liu, Q She, AI McLeod, B Wang
CVPR 2022, 16682-16691, 2022
Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs
S Liu, AV Masurkar, H Rusinek, J Chen, B Zhang, W Zhu, ...
Scientific reports 12 (1), 17106, 2022
Are all losses created equal: A neural collapse perspective
J Zhou, C You, X Li, K Liu, S Liu, Q Qu, Z Zhu
Advances in Neural Information Processing Systems 35, 31697-31710, 2022
Convolutional normalization: Improving deep convolutional network robustness and training
S Liu, X Li, Y Zhai, C You, Z Zhu, C Fernandez-Granda, Q Qu
35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021
Deep probability estimation
S Liu, A Kaku, W Zhu, M Leibovich, S Mohan, B Yu, L Zanna, N Razavian, ...
ICML 2022, 2021
Few-shot fine-grained action recognition via bidirectional attention and contrastive meta-learning
J Wang, Y Wang, S Liu, A Li
Proceedings of the 29th ACM International Conference on Multimedia, 582-591, 2021
Sparse recovery beyond compressed sensing: Separable nonlinear inverse problems
B Bernstein, S Liu, C Papadaniil, C Fernandez-Granda
IEEE transactions on information theory 66 (9), 5904-5926, 2020
Principled and efficient transfer learning of deep models via neural collapse
X Li, S Liu, J Zhou, X Lu, C Fernandez-Granda, Z Zhu, Q Qu
arXiv preprint arXiv:2212.12206, 2022
Paddles: Phase-amplitude spectrum disentangled early stopping for learning with noisy labels
H Huang, H Kang, S Liu, O Salvado, T Rakotoarivelo, D Wang, T Liu
arXiv preprint arXiv:2212.03462, 2022
Multiple instance learning via iterative self-paced supervised contrastive learning
K Liu, W Zhu, Y Shen, S Liu, N Razavian, KJ Geras, C Fernandez-Granda
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data
S Liu, M Cheng, H Brooks, W Mackey, DJ Heeger, EG Tabak, ...
2019 NeurIPS Machine Learning for Health Workshop, 2019
Avoiding spurious correlations via logit correction
S Liu, X Zhang, N Sekhar, Y Wu, P Singhal, C Fernandez-Granda
arXiv preprint arXiv:2212.01433, 2022
Development of a Deep Learning Model for Early Alzheimer’s Disease Detection from Structural MRIs and External Validation on an Independent Cohort
S Liu, AV Masurkar, H Rusinek, J Chen, B Zhang, W Zhu, ...
Lower Bounds for Mutual Information in Representation Learning
S Liu
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
H Kang, S Liu, H Huang, J Yu, B Han, D Wang, T Liu
arXiv preprint arXiv:2307.05025, 2023
What Deep Representations Should We Learn?--A Neural Collapse Perspective
X Li, S Liu, J Zhou, C Fernandez-Granda, Z Zhu, Q Qu
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