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Aurelien Lucchi

ETH Zurich

Neural Symbolic Regression that Scales


Jun 11, 2021
Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo

* Accepted at the 38th International Conference on Machine Learning (ICML) 2021 

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Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks


Jun 07, 2021
Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurelien Lucchi


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Learning Generative Models of Textured 3D Meshes from Real-World Images


Mar 29, 2021
Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurelien Lucchi


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Generative Minimization Networks: Training GANs Without Competition


Mar 23, 2021
Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurelien Lucchi, Thomas Hofmann


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Direct-Search for a Class of Stochastic Min-Max Problems


Feb 22, 2021
Sotiris Anagnostidis, Aurelien Lucchi, Youssef Diouane


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Scalable Graph Networks for Particle Simulations


Oct 14, 2020
Karolis Martinkus, Aurelien Lucchi, Nathanaël Perraudin

* 19 pages, 20 figures 

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An Accelerated DFO Algorithm for Finite-sum Convex Functions


Jul 07, 2020
Yuwen Chen, Antonio Orvieto, Aurelien Lucchi

* 48 pages, 44 figures; Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020 

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Randomized Block-Diagonal Preconditioning for Parallel Learning


Jun 24, 2020
Celestine Mendler-Dünner, Aurelien Lucchi

* accepted at ICML 2020 

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Convolutional Generation of Textured 3D Meshes


Jun 13, 2020
Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurelien Lucchi


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Emulation of cosmological mass maps with conditional generative adversarial networks


Apr 17, 2020
Nathanaël Perraudin, Sandro Marcon, Aurelien Lucchi, Tomasz Kacprzak

* Accepted at the Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), December 14, 2019, https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_97.pdf 

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Theoretical Understanding of Batch-normalization: A Markov Chain Perspective


Mar 09, 2020
Hadi Daneshmand, Jonas Kohler, Francis Bach, Thomas Hofmann, Aurelien Lucchi


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Controlling Style and Semantics in Weakly-Supervised Image Generation


Dec 06, 2019
Dario Pavllo, Aurelien Lucchi, Thomas Hofmann


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A Stochastic Tensor Method for Non-convex Optimization


Nov 23, 2019
Aurelien Lucchi, Jonas Kohler


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Shadowing Properties of Optimization Algorithms


Nov 12, 2019
Antonio Orvieto, Aurelien Lucchi

* Advances in neural information processing systems. 2019 

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Cosmological N-body simulations: a challenge for scalable generative models


Aug 15, 2019
Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Réfrégier


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The Role of Memory in Stochastic Optimization


Jul 02, 2019
Antonio Orvieto, Jonas Kohler, Aurelien Lucchi

* Accepted paper at the 35th Conference on Uncertainty in Artificial Intelligence (UAI), Tel Aviv, 2019 

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Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training


May 22, 2019
Leonard Adolphs, Jonas Kohler, Aurelien Lucchi


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Evaluating GANs via Duality


Nov 13, 2018
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Nathanael Perraudin, Thomas Hofmann, Andreas Krause


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Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization


Oct 06, 2018
Jonas Kohler, Hadi Daneshmand, Aurelien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann


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Fast Cosmic Web Simulations with Generative Adversarial Networks


Sep 20, 2018
Andres C. Rodriguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphael Sgier, Janis Fluri, Thomas Hofmann, Alexandre Réfrégier


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Escaping Saddles with Stochastic Gradients


Sep 16, 2018
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi, Thomas Hofmann


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Fast Point Spread Function Modeling with Deep Learning


Jul 25, 2018
Jörg Herbel, Tomasz Kacprzak, Adam Amara, Alexandre Refregier, Aurelien Lucchi

* 25 pages, 8 figures, 1 table 

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Local Saddle Point Optimization: A Curvature Exploitation Approach


Jun 26, 2018
Leonard Adolphs, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann


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A Distributed Second-Order Algorithm You Can Trust


Jun 20, 2018
Celestine Dünner, Aurelien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi

* appearing at ICML 2018 - Proceedings of the 35th International Conference on Machine Learning, Stockholm, Schweden, PMLR 80, 2018 

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Adversarially Robust Training through Structured Gradient Regularization


May 22, 2018
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann


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Semantic Interpolation in Implicit Models


Feb 02, 2018
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann


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Flexible Prior Distributions for Deep Generative Models


Jan 07, 2018
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann

* arXiv admin note: text overlap with arXiv:1707.09241 

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Stabilizing Training of Generative Adversarial Networks through Regularization


Nov 07, 2017
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, Thomas Hofmann


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Learning Aerial Image Segmentation from Online Maps


Jul 21, 2017
Pascal Kaiser, Jan Dirk Wegner, Aurelien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler

* Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 

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