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Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes


Apr 23, 2021
James Lucas, Juhan Bae, Michael R. Zhang, Stanislav Fort, Richard Zemel, Roger Grosse

* 15 pages in main paper, 4 pages of references, 24 pages in appendix. 29 figures in total 

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Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel


Oct 28, 2020
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli

* 19 pages, 19 figures, In Advances in Neural Information Processing Systems 34 (NeurIPS 2020) 

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Training independent subnetworks for robust prediction


Oct 13, 2020
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew M. Dai, Dustin Tran


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The Break-Even Point on Optimization Trajectories of Deep Neural Networks


Feb 21, 2020
Stanislaw Jastrzebski, Maciej Szymczak, Stanislav Fort, Devansh Arpit, Jacek Tabor, Kyunghyun Cho, Krzysztof Geras

* Accepted as a spotlight at ICLR 2020. The last two authors contributed equally 

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Deep Ensembles: A Loss Landscape Perspective


Dec 05, 2019
Stanislav Fort, Huiyi Hu, Balaji Lakshminarayanan


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Emergent properties of the local geometry of neural loss landscapes


Oct 14, 2019
Stanislav Fort, Surya Ganguli

* 10 pages, 8 figures 

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Large Scale Structure of Neural Network Loss Landscapes


Jun 11, 2019
Stanislav Fort, Stanislaw Jastrzebski

* Submitted for review 

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Stiffness: A New Perspective on Generalization in Neural Networks


Jan 28, 2019
Stanislav Fort, Paweł Krzysztof Nowak, Srini Narayanan


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Adaptive Quantum State Tomography with Neural Networks


Dec 17, 2018
Yihui Quek, Stanislav Fort, Hui Khoon Ng

* First two authors (Yihui Quek and Stanislav Fort) contributed equally. 13 pages, 10 figures 

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The Goldilocks zone: Towards better understanding of neural network loss landscapes


Jul 06, 2018
Stanislav Fort, Adam Scherlis

* 14 pages, 14 figures. A subset of the paper accepted at Modern Trends in Nonconvex Optimization for Machine Learning workshop at the 35th International Conference on Machine Learning (ICML 2018) 

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Towards understanding feedback from supermassive black holes using convolutional neural networks


Dec 02, 2017
Stanislav Fort

* 5 pages, 5 figures, accepted at Workshop on Deep Learning for Physical Sciences (DLPS 2017), NIPS 2017, Long Beach, CA, USA 

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Gaussian Prototypical Networks for Few-Shot Learning on Omniglot


Aug 09, 2017
Stanislav Fort


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