Libin Zhu
Libin Zhu
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On the linearity of large non-linear models: when and why the tangent kernel is constant
C Liu, L Zhu, M Belkin
Advances in Neural Information Processing Systems 33, 15954-15964, 2020
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
C Liu, L Zhu, M Belkin
Applied and Computational Harmonic Analysis 59, 85-116, 2022
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning
C Liu, L Zhu, M Belkin
arXiv preprint arXiv:2003.00307, 2020
Quadratic models for understanding neural network dynamics
L Zhu, C Liu, A Radhakrishnan, M Belkin
arXiv preprint arXiv:2205.11787, 2022
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture
L Zhu, C Liu, M Belkin
arXiv preprint arXiv:2205.11786, 2022
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models
C Liu, L Zhu, M Belkin
arXiv preprint arXiv:2203.05104, 2022
Restricted Strong Convexity of Deep Learning Models with Smooth Activations
A Banerjee, P Cisneros-Velarde, L Zhu, M Belkin
arXiv preprint arXiv:2209.15106, 2022
A note on Linear Bottleneck networks and their Transition to Multilinearity
L Zhu, P Pandit, M Belkin
arXiv preprint arXiv:2206.15058, 2022
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