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Posts
1-Lipschitz Layers Compared
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portfolio
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Portfolio item number 2
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publications
Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks
Published in ECCV, 2022
We introduce a rescaling-based parameterization that guarantees linear (fully-connected and convolutional) layers to be 1-Lipschitz
1-Lipschitz Neural Networks are more expressive with N-Activations
Published in arXiv, 2023
We show a shortcoming with currently popular activation functions in 1-Lipschitz networks, both empirically and experimentally. Then we propose an activation function that provably overcomes this limitation.
1-Lipschitz Layers Compared: Memory, Speed and Certifiable Robustness
Published in CVPR, 2024
We compare existing methods of creating 1-Lipschitz convolutions, analysing them both theoretically as well as experimentally.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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