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Libin Zhu
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2020 – today
- 2024
- [b1]Libin Zhu:
Toward Understanding the Dynamics of Over-parameterized Neural Networks. University of California, San Diego, USA, 2024 - [c7]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Quadratic models for understanding catapult dynamics of neural networks. ICLR 2024 - [c6]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. ICML 2024 - [i9]Neil Mallinar, Daniel Beaglehole, Libin Zhu, Adityanarayanan Radhakrishnan, Parthe Pandit, Mikhail Belkin:
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product. CoRR abs/2407.20199 (2024) - 2023
- [c5]Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Misha Belkin:
Restricted Strong Convexity of Deep Learning Models with Smooth Activations. ICLR 2023 - [c4]Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin:
Neural tangent kernel at initialization: linear width suffices. UAI 2023: 110-118 - [i8]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning. CoRR abs/2306.04815 (2023) - 2022
- [c3]Chaoyue Liu, Libin Zhu, Misha Belkin:
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models. ICLR 2022 - [c2]Libin Zhu, Chaoyue Liu, Misha Belkin:
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. NeurIPS 2022 - [i7]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models. CoRR abs/2203.05104 (2022) - [i6]Libin Zhu, Chaoyue Liu, Mikhail Belkin:
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture. CoRR abs/2205.11786 (2022) - [i5]Libin Zhu, Chaoyue Liu, Adityanarayanan Radhakrishnan, Mikhail Belkin:
Quadratic models for understanding neural network dynamics. CoRR abs/2205.11787 (2022) - [i4]Libin Zhu, Parthe Pandit, Mikhail Belkin:
A note on Linear Bottleneck networks and their Transition to Multilinearity. CoRR abs/2206.15058 (2022) - [i3]Arindam Banerjee, Pedro Cisneros-Velarde, Libin Zhu, Mikhail Belkin:
Restricted Strong Convexity of Deep Learning Models with Smooth Activations. CoRR abs/2209.15106 (2022) - 2020
- [c1]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
On the linearity of large non-linear models: when and why the tangent kernel is constant. NeurIPS 2020 - [i2]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning. CoRR abs/2003.00307 (2020) - [i1]Chaoyue Liu, Libin Zhu, Mikhail Belkin:
On the linearity of large non-linear models: when and why the tangent kernel is constant. CoRR abs/2010.01092 (2020)
Coauthor Index
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