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CoRR abs\/1406.1831(2014). arXiv:1406.1831  Ben Poole Jascha Sohl-Dickstein and Surya Ganguli. 2014. Analyzing noise in autoencoders and deep networks. CoRR abs\/1406.1831(2014). arXiv:1406.1831"},{"key":"e_1_3_2_1_38_1","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","author":"Qi Charles\u00a0Ruizhongtai","year":"2017","unstructured":"Charles\u00a0Ruizhongtai Qi , Hao Su , Kaichun Mo , and Leonidas\u00a0 J. Guibas . 2017 . PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation . In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , Honolulu, HI, USA , July 21-26, 2017. IEEE Computer Society, 77\u201385. Charles\u00a0Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas\u00a0J. Guibas. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. 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In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016 , December 5-10, 2016, Barcelona, Spain, Daniel\u00a0D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 901. Tim Salimans and Diederik\u00a0P. Kingma. 2016. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, Daniel\u00a0D. 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Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_1_45_1","volume-title":"Adversarial Lipschitz Regularization. In 8th International Conference on Learning Representations, ICLR 2020","author":"Terj\u00e9k D\u00e1vid","year":"2020","unstructured":"D\u00e1vid Terj\u00e9k . 2020 . Adversarial Lipschitz Regularization. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia , April 26-30, 2020. OpenReview.net. D\u00e1vid Terj\u00e9k. 2020. Adversarial Lipschitz Regularization. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. 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In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446\u20139454 . Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2018. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 9446\u20139454."},{"key":"e_1_3_2_1_49_1","unstructured":"D\u00e1niel Varga Adri\u00e1n Csisz\u00e1rik and Zsolt Zombori. 2017. Gradient regularization improves accuracy of discriminative models. arXiv preprint arXiv:1712.09936(2017).  D\u00e1niel Varga Adri\u00e1n Csisz\u00e1rik and Zsolt Zombori. 2017. Gradient regularization improves accuracy of discriminative models. arXiv preprint arXiv:1712.09936(2017)."},{"key":"e_1_3_2_1_50_1","volume-title":"Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Virmaux Aladin","year":"2018","unstructured":"Aladin Virmaux and Kevin Scaman . 2018 . Lipschitz regularity of deep neural networks: analysis and efficient estimation . In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018 , NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna\u00a0M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 3839\u20133848. Aladin Virmaux and Kevin Scaman. 2018. Lipschitz regularity of deep neural networks: analysis and efficient estimation. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna\u00a0M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 3839\u20133848."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_4"},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan","author":"Weng Tsui-Wei","year":"2018","unstructured":"Tsui-Wei Weng , Huan Zhang , Hongge Chen , Zhao Song , Cho-Jui Hsieh , Luca Daniel , Duane\u00a0 S. Boning , and Inderjit\u00a0 S. Dhillon . 2018 . Towards Fast Computation of Certified Robustness for ReLU Networks . In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan , Stockholm, Sweden , July 10-15, 2018(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer\u00a0G. Dy and Andreas Krause (Eds.). PMLR, 5273\u20135282. Tsui-Wei Weng, Huan Zhang, Hongge Chen, Zhao Song, Cho-Jui Hsieh, Luca Daniel, Duane\u00a0S. Boning, and Inderjit\u00a0S. Dhillon. 2018. Towards Fast Computation of Certified Robustness for ReLU Networks. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer\u00a0G. Dy and Andreas Krause (Eds.). PMLR, 5273\u20135282."},{"key":"e_1_3_2_1_53_1","volume-title":"Deep Geometric Prior for Surface Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019","author":"Williams Francis","year":"2019","unstructured":"Francis Williams , Teseo Schneider , Cl\u00e1udio\u00a0 T. Silva , Denis Zorin , Joan Bruna , and Daniele Panozzo . 2019 . Deep Geometric Prior for Surface Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019 , Long Beach, CA, USA , June 16-20, 2019. Computer Vision Foundation \/ IEEE, 10130\u201310139. Francis Williams, Teseo Schneider, Cl\u00e1udio\u00a0T. Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo. 2019. Deep Geometric Prior for Surface Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019. Computer Vision Foundation \/ IEEE, 10130\u201310139."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00982"},{"key":"e_1_3_2_1_55_1","unstructured":"Yiheng Xie Towaki Takikawa Shunsuke Saito Or Litany Shiqin Yan Numair Khan Federico Tombari James Tompkin Vincent Sitzmann and Srinath Sridhar. 2021. Neural Fields in Visual Computing and Beyond. CoRR abs\/2111.11426(2021).  Yiheng Xie Towaki Takikawa Shunsuke Saito Or Litany Shiqin Yan Numair Khan Federico Tombari James Tompkin Vincent Sitzmann and Srinath Sridhar. 2021. Neural Fields in Visual Computing and Beyond. CoRR abs\/2111.11426(2021)."},{"key":"e_1_3_2_1_56_1","unstructured":"Yuichi Yoshida and Takeru Miyato. 2017. Spectral Norm Regularization for Improving the Generalizability of Deep Learning. CoRR abs\/1705.10941(2017). arXiv:1705.10941  Yuichi Yoshida and Takeru Miyato. 2017. Spectral Norm Regularization for Improving the Generalizability of Deep Learning. 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