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The loss landscapes of deep neural networks are not well understood due to their high nonconvexity.
The hungarian method for the assignment problem
Kuhn, H. W · 1955
Earlier work this paper cites.
On the geometry of feedforward neural network error surfaces
Chen, A. M., Lu, H.-m., and Hecht-Nielsen, R · 1993
Earlier work this paper cites.
Linear assignment problems and extensions
Burkard, R. E. and Cela, E · 1999
Earlier work this paper cites.
O-minimal preparation theorems
van den Dries, L. and Speissegger, P · 2002
Earlier work this paper cites.
Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain
Avants, B. B., Epstein, C. L., Grossman, M., and Gee, J. C · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the kurdyka-łojasiewicz inequality
Attouch, H., Bolte, J., Redont, P., and Soubeyran, A · 2010
Earlier work this paper cites.
The loss surfaces of multilayer networks, 2014
Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., and LeCun, Y · 2014
Earlier work this paper cites.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, 2014
Dauphin, Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., and Bengio, Y · 2014
Earlier work this paper cites.
Qualitatively characterizing neural network optimization problems, 2014
Goodfellow, I. J., Vinyals, O., and Saxe, A. M · 2014
Earlier work this paper cites.
Explorations on high dimensional landscapes, 2014
Sagun, L., Guney, V. U., Arous, G. B., and LeCun, Y · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
On the quality of the initial basin in overspecified neural networks, 2015
Safran, I. and Shamir, O · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
Notes on birkhoff–von neumann decomposition of doubly stochastic matrices
Dufossé, F. and Uçar, B · 2016
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Topology and geometry of half-rectified network optimization
Freeman, C. D. and Bruna, J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Convergent learning: Do different neural networks learn the same representations?
Loss surfaces, mode connectivity, and fast ensembling of dnns
Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D. P., and Wilson, A. G · 2018
Later among the works it cites.
Using mode connectivity for loss landscape analysis
Gotmare, A., Keskar, N. S., Xiong, C., and Socher, R · 2018
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Insights on representational similarity in neural networks with canonical correlation
Morcos, A., Raghu, M., and Bengio, S · 2018
Later among the works it cites.
Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Su, D., Zhang, H., Chen, H., Yi, J., Chen, P.-Y., and Gao, Y · 2018
Later among the works it cites.
Towards understanding learning representations: To what extent do different neural networks learn the same representation, 2018
Wang, L., Hu, L., Gu, J., Wu, Y., Hu, Z., He, K., and Hopcroft, J · 2018
Later among the works it cites.
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Li, Y., Yosinski, J., Clune, J., Lipson, H., and Hopcroft, J. E · 2016
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Proximal gradient method for huberized support vector machine
Xu, Y., Akrotirianakis, I., and Chakraborty, A · 2016
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Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Riemannian approach to batch normalization
Cho, M. and Lee, J · 2017
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Towards deep learning models resistant to adversarial attacks, 2017
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Skip connections eliminate singularities, 2017
Orhan, A. E. and Pitkow, X · 2017
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Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
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Brea, J., Simsek, B., Illing, B., and Gerstner, W · 2019
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Similarity of neural network representations revisited, 2019
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G · 2019
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Explaining landscape connectivity of low-cost solutions for multilayer nets, 2019
Kuditipudi, R., Wang, X., Lee, H., Zhang, Y., Li, Z., Hu, W., Arora, S., and Ge, R · 2019
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Robustness via curvature regularization, and vice versa
Moosavi-Dezfooli, S.-M., Fawzi, A., Uesato, J., and Frossard, P · 2019
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Computational optimal transport
Peyré, G., Cuturi, M., et al · 2019
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Model fusion via optimal transport, 2019
Singh, S. P. and Jaggi, M · 2019
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Overfitting in adversarially robust deep learning
Rice, L., Wong, E., and Kolter, J. Z · 2020
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Bridging mode connectivity in loss landscapes and adversarial robustness
Zhao, P., Chen, P.-Y., Das, P., Ramamurthy, K. N., and Lin, X · 2020
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