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@@ -83,3 +83,46 @@ issn = {2542-4653} } +@article{Suryadevara_2024_The, + author = {Suryadevara, Praharsh and Casiulis, Mathias and Martiniani, Stefano}, + title = {The Basins of Attraction of Soft Sphere Packings Are Not Fractal}, + year = {2024}, + month = {sep}, + url = {http://arxiv.org/abs/2409.12113v2}, + date = {2024-09-18T16:32:49Z}, + eprint = {2409.12113v2}, + eprintclass = {cond-mat.stat-mech}, + eprinttype = {arxiv}, + urldate = {2025-07-23T12:20:26.278408Z} +} + +@inproceedings{Draxler_2018_Essentially, + author = {Draxler, Felix and Veschgini, Kambis and Salmhofer, Manfred and Hamprecht, Fred}, + title = {Essentially No Barriers in Neural Network Energy Landscape}, + publisher = {PMLR}, + year = {2018}, + month = {10--15 Jul}, + volume = {80}, + pages = {1309--1318}, + url = {https://proceedings.mlr.press/v80/draxler18a.html}, + abstract = {Training neural networks involves finding minima of a high-dimensional non-convex loss function. Relaxing from linear interpolations, we construct continuous paths between minima of recent neural network architectures on CIFAR10 and CIFAR100. Surprisingly, the paths are essentially flat in both the training and test landscapes. This implies that minima are perhaps best seen as points on a single connected manifold of low loss, rather than as the bottoms of distinct valleys.}, + booktitle = {Proceedings of the 35th International Conference on Machine Learning}, + editor = {Dy, Jennifer and Krause, Andreas}, + pdf = {http://proceedings.mlr.press/v80/draxler18a/draxler18a.pdf}, + series = {Proceedings of Machine Learning Research} +} + +@article{Liu_2022_Loss, + author = {Liu, Chaoyue and Zhu, Libin and Belkin, Mikhail}, + title = {Loss landscapes and optimization in over-parameterized non-linear systems and neural networks}, + journal = {Applied and Computational Harmonic Analysis}, + publisher = {Elsevier BV}, + year = {2022}, + month = {July}, + volume = {59}, + pages = {85--116}, + url = {http://dx.doi.org/10.1016/j.acha.2021.12.009}, + doi = {10.1016/j.acha.2021.12.009}, + issn = {1063-5203} +} + |