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SysML: The New Frontier of Machine Learning Systems.

A. Ratner, D. Alistarh, G. Alonso, D. Andersen, P. Bailis, S. Bird, N. Carlini, B. Catanzaro, E. Chung, B. Dally, J. Dean, I. Dhillon, A. Dimakis, P. Dubey, C. Elkan, G. Fursin, G. Ganger, L. Getoor, P. Gibbons, G. Gibson, J. Gonzalez, J. Gottschlich, S. Han, K. Hazelwood, F. Huang, M. Jaggi, K. Jamieson, M. Jordan, G. Joshi, R. Khalaf, J. Knight, J. Konecný, T. Kraska, A. Kumar, A. Kyrillidis, J. Li, S. Madden, H. McMahan, E. Meijer, I. Mitliagkas, R. Monga, D. Murray, D. Papailiopoulos, G. Pekhimenko, T. Rekatsinas, A. Rostamizadeh, C. Ré, C. Sa, H. Sedghi, S. Sen, V. Smith, A. Smola, D. Song, E. Sparks, I. Stoica, V. Sze, M. Udell, J. Vanschoren, S. Venkataraman, R. Vinayak, M. Weimer, A. Wilson, E. Xing, M. Zaharia, C. Zhang, and A. Talwalkar. CoRR, (2019)

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PD Dr. Ioannis Doltsinis University of Stuttgart

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Deep learning at 15PF: supervised and semi-supervised classification for scientific data.T. Kurth, J. Zhang, N. Satish, E. Racah, I. Mitliagkas, M. Patwary, T. Malas, N. Sundaram, W. Bhimji, M. Smorkalov and 5 other author(s). SC, page 7:1-7:11. ACM, (2017)Improving Gibbs Sampler Scan Quality with DoGS.I. Mitliagkas, and L. Mackey. ICML, volume 70 of Proceedings of Machine Learning Research, page 2469-2477. PMLR, (2017)Adversarial target-invariant representation learning for domain generalization.I. Albuquerque, J. Monteiro, T. Falk, and I. Mitliagkas. CoRR, (2019)Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer.V. Verma, A. Lamb, C. Beckham, A. Courville, I. Mitliagkas, and Y. Bengio. CoRR, (2018)Learning Representations and Generative Models for 3D Point Clouds.P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas. ICML, volume 80 of Proceedings of Machine Learning Research, page 40-49. PMLR, (2018)Negative Momentum for Improved Game Dynamics.G. Gidel, R. Hemmat, M. Pezeshki, R. Priol, G. Huang, S. Lacoste-Julien, and I. Mitliagkas. AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1802-1811. PMLR, (2019)Multi-objective training of Generative Adversarial Networks with multiple discriminators.I. Albuquerque, J. Monteiro, T. Doan, B. Considine, T. Falk, and I. Mitliagkas. ICML, volume 97 of Proceedings of Machine Learning Research, page 202-211. PMLR, (2019)A Modern Take on the Bias-Variance Tradeoff in Neural Networks.B. Neal, S. Mittal, A. Baratin, V. Tantia, M. Scicluna, S. Lacoste-Julien, and I. Mitliagkas. CoRR, (2018)Accelerated Stochastic Power Iteration.C. Sa, B. He, I. Mitliagkas, C. Ré, and P. Xu. CoRR, (2017)Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations.A. Lamb, J. Binas, A. Goyal, D. Serdyuk, S. Subramanian, I. Mitliagkas, and Y. Bengio. CoRR, (2018)

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Disambiguation of "Mitliagkas, Ioannis"

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SysML: The New Frontier of Machine Learning Systems.

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PD Dr. Ioannis Doltsinis University of Stuttgart

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Disambiguation of "Mitliagkas, Ioannis"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML SysML: The New Frontier of Machine Learning Systems.

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PD Dr. Ioannis Doltsinis University of Stuttgart

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SysML: The New Frontier of Machine Learning Systems.