Следене
Zhenmei Shi
Заглавие
Позовавания
Позовавания
Година
SF-Net: Structured feature network for continuous sign language recognition
Z Yang*, Z Shi*, X Shen, YW Tai
arXiv preprint arXiv:1908.01341, 2019
792019
A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Z Shi*, J Wei*, Y Liang
ICLR 2022: International Conference on Learning Representations, 2022
462022
Deep Online Fused Video Stabilization
Z Shi, F Shi, WS Lai, CK Liang, Y Liang
WACV 2022: Winter Conference on Applications of Computer Vision, 2022
222022
The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning
Z Shi*, J Chen*, K Li, J Raghuram, X Wu, Y Liang, S Jha
ICLR 2023 (Spotlight): International Conference on Learning Representations, 2023
212023
When and How Does Known Class Help Discover Unknown Ones? Provable Understandings Through Spectral Analysis
Y Sun, Z Shi, Y Liang, Y Li
ICML 2023: International Conference on Machine Learning, 2023
132023
Attentive walk-aggregating graph neural networks
MF Demirel, S Liu, S Garg, Z Shi, Y Liang
Transactions on Machine Learning Research, 2022
13*2022
Domain generalization via nuclear norm regularization
Z Shi, Y Ming, Y Fan, F Sala, Y Liang
Conference on Parsimony and Learning, 179-201, 2024
11*2024
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
Z Xu, Z Shi, J Wei, F Mu, Y Li, Y Liang
ICLR 2024: International Conference on Learning Representations, 2024
10*2024
Provable Guarantees for Neural Networks via Gradient Feature Learning
Z Shi*, J Wei*, Y Liang
NeurIPS 2023: Neural Information Processing Systems, 2023
62023
A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learning
Y Sun, Z Shi, Y Li
NeurIPS 2023 (Spotlight): Neural Information Processing Systems, 2023
52023
Conv-basis: A new paradigm for efficient attention inference and gradient computation in transformers
J Gu*, Y Liang*, H Liu*, Z Shi*, Z Song*, J Yin*
arXiv preprint arXiv:2405.05219, 2024
42024
Fourier Circuits in Neural Networks: Unlocking the Potential of Large Language Models in Mathematical Reasoning and Modular Arithmetic
J Gu*, C Li*, Y Liang*, Z Shi*, Z Song*, T Zhou*
arXiv preprint arXiv:2402.09469, 2024
42024
Do Large Language Models Have Compositional Ability? An Investigation into Limitations and Scalability
Z Xu*, Z Shi*, Y Liang
ME-FoMo: Mathematical and Empirical Understanding of Foundation Models, 2024
32024
Tensor Attention Training: Provably Efficient Learning of Higher-order Transformers
J Gu*, Y Liang*, Z Shi*, Z Song*, Y Zhou*
arXiv preprint arXiv:2405.16411, 2024
22024
Exploring the Frontiers of Softmax: Provable Optimization, Applications in Diffusion Model, and Beyond
J Gu*, C Li*, Y Liang*, Z Shi*, Z Song*
arXiv preprint arXiv:2405.03251, 2024
22024
DAWN: Dual Augmented Memory Network for Unsupervised Video Object Tracking
Z Shi*, H Fang*, YW Tai, CK Tang
arXiv preprint arXiv:1908.00777, 2019
22019
Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
J Gu*, Y Liang*, Z Shi*, Z Song*, Y Zhou*
arXiv preprint arXiv:2405.16418, 2024
2024
Why Larger Language Models Do In-context Learning Differently?
Z Shi, J Wei, Z Xu, Y Liang
ICML 2024: International Conference on Machine Learning, 2024
2024
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