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We propose an efficient algorithm to visualise symmetries in neural networks.
Uniqueness of the weights for minimal feedforward nets with a given input-output map
Héctor J. Sussmann · 1992
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For neural networks, function determines form
Francesca Albertini and Eduardo D Sontag · 1993
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Uniqueness of weights for neural networks
Francesca Albertini, Eduardo D Sontag, and Vincent Maillot · 1993
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Natural gradient works efficiently in learning
Shun-Ichi Amari · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Statistical inference
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A survey on transfer learning
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Introduction to Smooth Manifolds
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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𝒢 \mathcal{G} -sgd: Optimizing relu neural networks in its positively scale-invariant space
Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, and Tie-Yan Liu · 2018
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Johanni Brea, Berfin Simsek, Bernd Illing, and Wulfram Gerstner · 2019
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Essentially no barriers in neural network energy landscape
Felix Draxler, Kambis Veschgini, Manfred Salmhofer, and Fred A Hamprecht · 2018
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Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey · 2020
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A foliated view of transfer learning
Janith Petangoda, Nick AM Monk, and Marc Peter Deisenroth · 2020
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Notes on the symmetries of 2-layer relu-networks
Henning Petzka, Martin Trimmel, and Cristian Sminchisescu · 2020
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