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2020 – today
- 2024
[c59]Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Proske, Aurélien Lucchi:
SDEs for Minimax Optimization. AISTATS 2024: 4834-4842- [c58]Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius:
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. ICML 2024 - [c57]Emanuele Francazi, Aurélien Lucchi, Marco Baity-Jesi:
Initial Guessing Bias: How Untrained Networks Favor Some Classes. ICML 2024 - [i60]Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius:
Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum. CoRR abs/2402.01297 (2024) - [i59]Enea Monzio Compagnoni, Antonio Orvieto, Hans Kersting, Frank Norbert Proske, Aurélien Lucchi:
SDEs for Minimax Optimization. CoRR abs/2402.12508 (2024) - [i58]Jim Zhao, Aurélien Lucchi, Nikita Doikov:
Cubic regularized subspace Newton for non-convex optimization. CoRR abs/2406.16666 (2024) - [i57]Rustem Islamov, Niccolò Ajroldi, Antonio Orvieto, Aurélien Lucchi:
Loss Landscape Characterization of Neural Networks without Over-Parametrization. CoRR abs/2410.12455 (2024) - [i56]Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, David Belius:
A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression. CoRR abs/2410.17796 (2024) - 2023
- [c56]Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann:
Mastering Spatial Graph Prediction of Road Networks. ICCV 2023: 5385-5395 - [c55]Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi:
A Theoretical Analysis of the Learning Dynamics under Class Imbalance. ICML 2023: 10285-10322 - [c54]Enea Monzio Compagnoni, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi:
An SDE for Modeling SAM: Theory and Insights. ICML 2023: 25209-25253 - [c53]Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann:
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. NeurIPS 2023 - [c52]Tin Sum Cheng, Aurélien Lucchi, Anastasis Kratsios, Ivan Dokmanic, David Belius:
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. NeurIPS 2023 - [i55]Enea Monzio Compagnoni
, Luca Biggio, Antonio Orvieto, Frank Norbert Proske, Hans Kersting, Aurélien Lucchi:
An SDE for Modeling SAM: Theory and Insights. CoRR abs/2301.08203 (2023) - [i54]Sotiris Anagnostidis, Dario Pavllo, Luca Biggio, Lorenzo Noci, Aurélien Lucchi, Thomas Hofmann:
Dynamic Context Pruning for Efficient and Interpretable Autoregressive Transformers. CoRR abs/2305.15805 (2023) - [i53]Emanuele Francazi, Aurélien Lucchi, Marco Baity-Jesi:
Initial Guessing Bias: How Untrained Networks Favor Some Classes. CoRR abs/2306.00809 (2023) - [i52]Xuwei Yang, Anastasis Kratsios, Florian Krach, Matheus R. Grasselli, Aurélien Lucchi:
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option Pricing. CoRR abs/2309.04557 (2023) - [i51]Tin Sum Cheng, Aurélien Lucchi, Ivan Dokmanic, Anastasis Kratsios, David Belius:
A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression. CoRR abs/2310.00987 (2023) - 2022
- [c51]Youssef Diouane, Aurélien Lucchi, Vihang Prakash Patil:
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. AISTATS 2022: 836-859 - [c50]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. AISTATS 2022: 5485-5517 - [c49]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature in Randomly Initialized Deep ReLU Networks. AISTATS 2022: 7942-7975 - [c48]Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi:
Generalization Through the Lens of Leave-One-Out Error. ICLR 2022 - [c47]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. ICLR 2022 - [c46]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. ICML 2022: 17094-17116 - [c45]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. NeurIPS 2022 - [c44]Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi:
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. NeurIPS 2022 - [i50]Antonio Orvieto, Hans Kersting, Frank Proske, Francis R. Bach, Aurélien Lucchi:
Anticorrelated Noise Injection for Improved Generalization. CoRR abs/2202.02831 (2022) - [i49]Youssef Diouane, Aurélien Lucchi, Vihang Patil:
A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learning. CoRR abs/2202.10464 (2022) - [i48]Gregor Bachmann, Thomas Hofmann, Aurélien Lucchi:
Generalization Through The Lens Of Leave-One-Out Error. CoRR abs/2203.03443 (2022) - [i47]Sidak Pal Singh, Aurélien Lucchi, Thomas Hofmann, Bernhard Schölkopf:
Phenomenology of Double Descent in Finite-Width Neural Networks. CoRR abs/2203.07337 (2022) - [i46]Lorenzo Noci, Sotiris Anagnostidis, Luca Biggio, Antonio Orvieto, Sidak Pal Singh, Aurélien Lucchi:
Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse. CoRR abs/2206.03126 (2022) - [i45]Emanuele Francazi, Marco Baity-Jesi, Aurélien Lucchi:
Characterizing the Effect of Class Imbalance on the Learning Dynamics. CoRR abs/2207.00391 (2022) - [i44]Aurélien Lucchi, Frank Proske, Antonio Orvieto, Francis R. Bach, Hans Kersting:
On the Theoretical Properties of Noise Correlation in Stochastic Optimization. CoRR abs/2209.09162 (2022) - [i43]Sotiris Anagnostidis, Aurélien Lucchi, Thomas Hofmann:
Mastering Spatial Graph Prediction of Road Networks. CoRR abs/2210.00828 (2022) - 2021
- [j8]Nathanaël Perraudin, Sandro Marcon, Aurélien Lucchi, Tomasz Kacprzak:
Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks. Frontiers Artif. Intell. 4: 673062 (2021) - [j7]Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, Stefan Woerner:
The power of quantum neural networks. Nat. Comput. Sci. 1(6): 403-409 (2021) - [j6]Christa Zoufal, Aurélien Lucchi, Stefan Woerner:
Variational quantum Boltzmann machines. Quantum Mach. Intell. 3(1): 1-15 (2021) - [c43]Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin:
Scalable Graph Networks for Particle Simulations. AAAI 2021: 8912-8920 - [c42]Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
Momentum Improves Optimization on Riemannian Manifolds. AISTATS 2021: 1351-1359 - [c41]Sotirios-Konstantinos Anagnostidis, Aurélien Lucchi, Youssef Diouane:
Direct-Search for a Class of Stochastic Min-Max Problems. AISTATS 2021: 3772-3780 - [c40]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. ICCV 2021: 13859-13869 - [c39]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that scales. ICML 2021: 936-945 - [c38]Aurélien Lucchi, Antonio Orvieto, Adamos Solomou:
On the Second-order Convergence Properties of Random Search Methods. NeurIPS 2021: 25633-25645 - [i42]Sotiris Anagnostidis, Aurélien Lucchi, Youssef Diouane:
Direct-Search for a Class of Stochastic Min-Max Problems. CoRR abs/2102.11386 (2021) - [i41]Paulina Grnarova, Yannic Kilcher, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann:
Generative Minimization Networks: Training GANs Without Competition. CoRR abs/2103.12685 (2021) - [i40]Dario Pavllo, Jonas Kohler, Thomas Hofmann, Aurélien Lucchi:
Learning Generative Models of Textured 3D Meshes from Real-World Images. CoRR abs/2103.15627 (2021) - [i39]Antonio Orvieto, Jonas Kohler, Dario Pavllo, Thomas Hofmann, Aurélien Lucchi:
Vanishing Curvature and the Power of Adaptive Methods in Randomly Initialized Deep Networks. CoRR abs/2106.03763 (2021) - [i38]Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurélien Lucchi, Giambattista Parascandolo:
Neural Symbolic Regression that Scales. CoRR abs/2106.06427 (2021) - [i37]Aurélien Lucchi, Antonio Orvieto, Adamos Solomou:
On the Second-order Convergence Properties of Random Search Methods. CoRR abs/2110.13265 (2021) - [i36]Junchi Yang, Antonio Orvieto, Aurélien Lucchi, Niao He:
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity. CoRR abs/2112.05604 (2021) - 2020
- [c37]Foivos Alimisis, Antonio Orvieto, Gary Bécigneul, Aurélien Lucchi:
A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization. AISTATS 2020: 1297-1307 - [c36]Dario Pavllo, Aurélien Lucchi, Thomas Hofmann:
Controlling Style and Semantics in Weakly-Supervised Image Generation. ECCV (6) 2020: 482-499 - [c35]Yuwen Chen, Antonio Orvieto, Aurélien Lucchi:
An Accelerated DFO Algorithm for Finite-sum Convex Functions. ICML 2020: 1681-1690 - [c34]Celestine Mendler-Dünner, Aurélien Lucchi:
Randomized Block-Diagonal Preconditioning for Parallel Learning. ICML 2020: 6841-6851 - [c33]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Batch normalization provably avoids ranks collapse for randomly initialised deep networks. NeurIPS 2020 - [c32]Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. NeurIPS 2020 - [i35]Hadi Daneshmand, Jonas Moritz Kohler, Francis R. Bach, Thomas Hofmann, Aurélien Lucchi:
Theoretical Understanding of Batch-normalization: A Markov Chain Perspective. CoRR abs/2003.01652 (2020) - [i34]Nathanaël Perraudin, Sandro Marcon, Aurélien Lucchi, Tomasz Kacprzak:
Emulation of cosmological mass maps with conditional generative adversarial networks. CoRR abs/2004.08139 (2020) - [i33]Dario Pavllo, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, Aurélien Lucchi:
Convolutional Generation of Textured 3D Meshes. CoRR abs/2006.07660 (2020) - [i32]Celestine Mendler-Dünner, Aurélien Lucchi:
Randomized Block-Diagonal Preconditioning for Parallel Learning. CoRR abs/2006.13591 (2020) - [i31]Yuwen Chen, Antonio Orvieto, Aurélien Lucchi:
An Accelerated DFO Algorithm for Finite-sum Convex Functions. CoRR abs/2007.03311 (2020) - [i30]Karolis Martinkus, Aurélien Lucchi, Nathanaël Perraudin:
Scalable Graph Networks for Particle Simulations. CoRR abs/2010.06948 (2020) - [i29]Amira Abbas, David Sutter, Christa Zoufal, Aurélien Lucchi, Alessio Figalli, Stefan Woerner:
The power of quantum neural networks. CoRR abs/2011.00027 (2020)
2010 – 2019
- 2019
- [c31]Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Local Saddle Point Optimization: A Curvature Exploitation Approach. AISTATS 2019: 486-495 - [c30]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Ming Zhou, Klaus Neymeyr:
Exponential convergence rates for Batch Normalization: The power of length-direction decoupling in non-convex optimization. AISTATS 2019: 806-815 - [c29]Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi:
Topological Map Extraction From Overhead Images. ICCV 2019: 1715-1724 - [c28]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause:
A Domain Agnostic Measure for Monitoring and Evaluating GANs. NeurIPS 2019: 12069-12079 - [c27]Antonio Orvieto, Aurélien Lucchi:
Continuous-time Models for Stochastic Optimization Algorithms. NeurIPS 2019: 12589-12601 - [c26]Antonio Orvieto, Aurélien Lucchi:
Shadowing Properties of Optimization Algorithms. NeurIPS 2019: 12671-12682 - [c25]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. UAI 2019: 356-366 - [i28]Leonard Adolphs, Jonas Kohler, Aurélien Lucchi:
Ellipsoidal Trust Region Methods and the Marginal Value of Hessian Information for Neural Network Training. CoRR abs/1905.09201 (2019) - [i27]Antonio Orvieto, Jonas Kohler, Aurélien Lucchi:
The Role of Memory in Stochastic Optimization. CoRR abs/1907.01678 (2019) - [i26]Nathanaël Perraudin, Ankit Srivastava, Aurélien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Réfrégier:
Cosmological N-body simulations: a challenge for scalable generative models. CoRR abs/1908.05519 (2019) - [i25]Antonio Orvieto, Aurélien Lucchi:
Shadowing Properties of Optimization Algorithms. CoRR abs/1911.05206 (2019) - [i24]Aurélien Lucchi, Jonas Kohler:
A Stochastic Tensor Method for Non-convex Optimization. CoRR abs/1911.10367 (2019) - [i23]Dario Pavllo, Aurélien Lucchi, Thomas Hofmann:
Controlling Style and Semantics in Weakly-Supervised Image Generation. CoRR abs/1912.03161 (2019) - 2018
- [c24]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. ICLR (Poster) 2018 - [c23]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Semantic Interpolation in Implicit Models. ICLR (Poster) 2018 - [c22]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. ICML 2018: 1163-1172 - [c21]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. ICML 2018: 1357-1365 - [i22]Hadi Daneshmand, Jonas Moritz Kohler, Aurélien Lucchi, Thomas Hofmann:
Escaping Saddles with Stochastic Gradients. CoRR abs/1803.05999 (2018) - [i21]Leonard Adolphs, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Local Saddle Point Optimization: A Curvature Exploitation Approach. CoRR abs/1805.05751 (2018) - [i20]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Adversarially Robust Training through Structured Gradient Regularization. CoRR abs/1805.08736 (2018) - [i19]Jonas Moritz Kohler, Hadi Daneshmand, Aurélien Lucchi, Ming Zhou, Klaus Neymeyr, Thomas Hofmann:
Towards a Theoretical Understanding of Batch Normalization. CoRR abs/1805.10694 (2018) - [i18]Celestine Dünner, Aurélien Lucchi, Matilde Gargiani, An Bian, Thomas Hofmann, Martin Jaggi:
A Distributed Second-Order Algorithm You Can Trust. CoRR abs/1806.07569 (2018) - [i17]Antonio Orvieto, Aurélien Lucchi:
Continuous-time Models for Stochastic Optimization Algorithms. CoRR abs/1810.02565 (2018) - [i16]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Nathanaël Perraudin, Thomas Hofmann, Andreas Krause:
Evaluating GANs via Duality. CoRR abs/1811.05512 (2018) - [i15]Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi:
PolyMapper: Extracting City Maps using Polygons. CoRR abs/1812.01497 (2018) - 2017
- [j5]Joël Akeret, Chihway L. Chang, Aurélien Lucchi, Alexandre Réfrégier:
Radio frequency interference mitigation using deep convolutional neural networks. Astron. Comput. 18: 35-39 (2017) - [j4]Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation From Online Maps. IEEE Trans. Geosci. Remote. Sens. 55(11): 6054-6068 (2017) - [c20]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. ICML 2017: 1895-1904 - [c19]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Stabilizing Training of Generative Adversarial Networks through Regularization. NIPS 2017: 2018-2028 - [c18]Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi:
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. WWW 2017: 1045-1052 - [i14]Jan Deriu, Aurélien Lucchi, Valeria De Luca, Aliaksei Severyn, Simon Müller, Mark Cieliebak, Thomas Hofmann, Martin Jaggi:
Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification. CoRR abs/1703.02504 (2017) - [i13]Jonas Moritz Kohler, Aurélien Lucchi:
Sub-sampled Cubic Regularization for Non-convex Optimization. CoRR abs/1705.05933 (2017) - [i12]Kevin Roth, Aurélien Lucchi, Sebastian Nowozin, Thomas Hofmann:
Stabilizing Training of Generative Adversarial Networks through Regularization. CoRR abs/1705.09367 (2017) - [i11]Paulina Grnarova, Kfir Y. Levy, Aurélien Lucchi, Thomas Hofmann, Andreas Krause:
An Online Learning Approach to Generative Adversarial Networks. CoRR abs/1706.03269 (2017) - [i10]Pascal Kaiser, Jan Dirk Wegner, Aurélien Lucchi, Martin Jaggi, Thomas Hofmann, Konrad Schindler:
Learning Aerial Image Segmentation from Online Maps. CoRR abs/1707.06879 (2017) - [i9]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Generator Reversal. CoRR abs/1707.09241 (2017) - [i8]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Semantic Interpolation in Implicit Models. CoRR abs/1710.11381 (2017) - [i7]Yannic Kilcher, Aurélien Lucchi, Thomas Hofmann:
Flexible Prior Distributions for Deep Generative Models. CoRR abs/1710.11383 (2017) - 2016
- [c17]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Starting Small - Learning with Adaptive Sample Sizes. ICML 2016: 1463-1471 - [c16]Aryan Mokhtari, Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann, Alejandro Ribeiro:
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy. NIPS 2016: 4062-4070 - [c15]Jan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurélien Lucchi, Valeria De Luca, Martin Jaggi:
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. SemEval@NAACL-HLT 2016: 1124-1128 - [c14]Octavian-Eugen Ganea, Marina Ganea, Aurélien Lucchi, Carsten Eickhoff, Thomas Hofmann:
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. WWW 2016: 927-938 - [i6]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
Starting Small - Learning with Adaptive Sample Sizes. CoRR abs/1603.02839 (2016) - [i5]Hadi Daneshmand, Aurélien Lucchi, Thomas Hofmann:
DynaNewton - Accelerating Newton's Method for Machine Learning. CoRR abs/1605.06561 (2016) - [i4]Wenhu Chen, Aurélien Lucchi, Thomas Hofmann:
Bootstrap, Review, Decode: Using Out-of-Domain Textual Data to Improve Image Captioning. CoRR abs/1611.05321 (2016) - 2015
- [j3]Aurélien Lucchi, Pablo Márquez-Neila, Carlos J. Becker, Yunpeng Li, Kevin Smith, Graham Knott, Pascal Fua:
Learning Structured Models for Segmentation of 2-D and 3-D Imagery. IEEE Trans. Medical Imaging 34(5): 1096-1110 (2015) - [c13]Thomas Hofmann, Aurélien Lucchi, Simon Lacoste-Julien, Brian McWilliams:
Variance Reduced Stochastic Gradient Descent with Neighbors. NIPS 2015: 2305-2313 - [i3]Aurélien Lucchi, Brian McWilliams, Thomas Hofmann:
A Variance Reduced Stochastic Newton Method. CoRR abs/1503.08316 (2015) - [i2]Thomas Hofmann, Aurélien Lucchi, Brian McWilliams:
Neighborhood Watch: Stochastic Gradient Descent with Neighbors. CoRR abs/1506.03662 (2015) - [i1]Octavian-Eugen Ganea, Marina Horlescu, Aurélien Lucchi, Carsten Eickhoff, Thomas Hofmann:
Probabilistic Bag-Of-Hyperlinks Model for Entity Linking. CoRR abs/1509.02301 (2015) - 2014
- [c12]Aurélien Lucchi, Carlos J. Becker, Pablo Márquez-Neila, Pascal Fua:
Exploiting Enclosing Membranes and Contextual Cues for Mitochondria Segmentation. MICCAI (1) 2014: 65-72 - 2013
- [b1]Aurélien Lucchi:
Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images. EPFL, Switzerland, 2013 - [c11]Aurélien Lucchi, Yunpeng Li, Pascal Fua:
Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets. CVPR 2013: 1987-1994 - [c10]Raphael Sznitman, Aurélien Lucchi, Peter I. Frazier, Bruno Jedynak, Pascal Fua:
An Optimal Policy for Target Localization with Application to Electron Microscopy. ICML (1) 2013: 1-9 - [c9]Raphael Sznitman, Aurélien Lucchi, Marco Cantoni, Graham Knott, Pascal Fua:
Flash Scanning Electron Microscopy. MICCAI (3) 2013: 413-420 - 2012
- [j2]Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurélien Lucchi, Pascal Fua, Sabine Süsstrunk:
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11): 2274-2282 (2012) - [j1]Aurélien Lucchi, Kevin Smith, Radhakrishna Achanta, Graham Knott, Pascal Fua:
Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks With Learned Shape Features. IEEE Trans. Medical Imaging 31(2): 474-486 (2012) - [c8]