La recherche joue un rôle majeur dans le domaine du Machine Learning. Avec des publications de premier ordre chaque année, l’EPFL jouit d’une excellente réputation.


Joint Human Pose Estimation and Stereo 3D Localization

W. Deng; L. Bertoni; S. Kreiss; A. Alahi 

2020-06-01. International Conference on Robotics and Automation (ICRA), paris, france, May 31th, June 4th 2020.

Noise-Resilient and Interpretable Epileptic Seizure Detection

A. Hitchcock Thomas; A. Aminifar; D. Atienza Alonso 

2020-05-17. IEEE International Symposium on Circuits and Systems – ISCAS 2020, Seville, Spain, May 17-21, 2020.

ExprADA: Adversarial domain adaptation for facial expression analysis

S. Bozorgtabar; D. Mahapatra; J-P. Thiran 

Pattern Recognition. 2020-04-01. Vol. 100, p. 107111. DOI : 10.1016/j.patcog.2019.107111.

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

M. El Helou; S. Süsstrunk 

2020-03-04. IEEE Transactions on Image Processing (TIP). p. 4885 – 4897. DOI : 10.1109/TIP.2020.2976814.

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis

P. Maggi; M. J. Fartaria; J. Jorge; F. La Rosa; M. Absinta et al. 

Nmr In Biomedicine. 2020-03-03.  p. e4283. DOI : 10.1002/nbm.4283.

Robust Reinforcement Learning via Adversarial training with Langevin Dynamics

K. Parameswaran; Y-T. Huang; Y-P. Hsieh; P. T. Y. Rolland; C. Shi et al. 


Collaborative Sampling in Generative Adversarial Networks

Y. Liu; P. A. Kothari; A. Alahi 

2020-02-11. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York, New York, USA, February 7-12, 2020.

Annealing and Replica-Symmetry in Deep Boltzmann Machines

D. Alberici; A. Barra; P. Contucci; E. Mingione 

Journal Of Statistical Physics. 2020-02-05. DOI : 10.1007/s10955-020-02495-2.

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

C. Capelo 


Vision based pixel-level bridge structural damage detection using a link ASPP network

W. Deng; Y. Mou; T. Kashiwa; S. Escalera; K. Nagai et al. 

Automation In Construction. 2020-02-01. Vol. 110, p. 102973. DOI : 10.1016/j.autcon.2019.102973.


A. Mohammadi; S. Bhattacharjee; S. Marcel 

2020. 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain,

Learning stereo reconstruction with deep neural networks

S. Tulyakov / F. Fleuret; A. Ivanov (Dir.)  

Lausanne, EPFL, 2020. 

Crowding and the Architecture of the Visual System

A. C. Doerig / M. Herzog (Dir.)  

Lausanne, EPFL, 2020. 

Exploration Methodology for BTI-Induced Failures on RRAM-Based Edge AI Systems

A. S. J. Levisse; M. A. Rios; M. Peon Quiros; D. Atienza Alonso 

2020. 45th International Conference on Acoustics, Speech, and Signal Processing _ ICASSP 2020, Barcelona, Spain, 4-6 May, 2020.

Byzantine machine learning

E. M. El Mhamdi; R. Guerraoui; S. Rouault; M. Taziki 



What graph neural networks cannot learn: depth vs width

A. Loukas 

2020. International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020.

Sparse and Parametric Modeling with Applications to Acoustics and Audio

H. Peic Tukuljac / P. Vandergheynst; H. Lissek (Dir.)  

Lausanne, EPFL, 2020. 

Multi-memristive synaptic architectures for training neural networks

I. Boybat Kara / Y. Leblebici; A. Sebastian (Dir.)  

Lausanne, EPFL, 2020. 

Evaluating the search phase of neural architecture search

K. Yu; C. Suito; M. Jaggi; C-C. Musat; M. Salzmann 

2020. ICRL 2020 Eighth International Conference on Learning Representations, Millennium Hall, Addis Ababa, ETHIOPIA, April 26-30, 2020.

Domain-Adaptive Multibranch Networks

R. Bermúdez Chacón; M. Salzmann; P. Fua 

2020. 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 26-30, 2020.


P. Motlicek; H. Hermansky; S. Madikeri; A. Prasad; S. Ganapathy 


On the Tunability of Optimizers in Deep Learning

P. T. Sivaprasad; F. Mai; T. Vogels; M. Jaggi; F. Fleuret 



Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

P. Gainza; F. Sverrisson; F. Monti; E. Rodolà; D. Boscaini et al. 

Nature Methods. 2019-12-09. Vol. 17, pages184–192(2020), p. 184-192. DOI : 10.1038/s41592-019-0666-6.

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

M. Unser; M. T. McCann 

Foundations and Trends® in Signal Processing. 2019-12-03. Vol. 13, num. 3, p. 280-359. DOI : 10.1561/2000000101.

Comparing dynamics: deep neural networks versus glassy systems

M. Baity-Jesi; L. Sagun; M. Geiger; S. Spigler; G. Ben Arpus et al. 

Journal Of Statistical Mechanics-Theory And Experiment. 2019-12-01. Vol. 2019, num. 12, p. 124013. DOI : 10.1088/1742-5468/ab3281.

Entropy and mutual information in models of deep neural networks

M. Gabrie; A. Manoel; C. Luneau; J. Barbier; N. Macris et al. 

Journal Of Statistical Mechanics-Theory And Experiment. 2019-12-01. Vol. 2019, num. 12, p. 124014. DOI : 10.1088/1742-5468/ab3430.

Latest advances in aging research and drug discovery

D. Bakula; A. Ablasser; A. Aguzzi; A. Antebi; N. Barzilai et al. 

Aging-Us. 2019-11-30. Vol. 11, num. 22, p. 9971-9981. DOI : 10.18632/aging.102487.

Le fabuleux chantier: Rendre l’intelligence artificielle robustement bénéfique

L. N. Hoang; E. M. El Mhamdi 

EDP Sciences, 2019-11-28.

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning

A. Mostajabi; H. Karami; M. Azadifar; A. Ghasemi; M. Rubinstein et al. 

Nature Scientific Reports. 2019-11-22. Vol. 9, num. 1. DOI : 10.1038/s41598-019-53934-4.

A jamming transition from under- to over-parametrization affects generalization in deep learning

S. Spigler; M. Geiger; S. d’Ascoli; L. Sagun; G. Biroli et al. 

Journal Of Physics A-Mathematical And Theoretical. 2019-11-22. Vol. 52, num. 47, p. 474001. DOI : 10.1088/1751-8121/ab4c8b.

Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks

R. Castello; S. Roquette; M. Esguerra; A. Guerra; J-L. Scartezzini 

2019-11-20. CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, 4–6 September 2019. DOI : 10.1088/1742-6596/1343/1/012034.

Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

W. Liu; K. M. Lis; M. Salzmann; P. Fua 

2019-11-08. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, November 4-8, 2019.

Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

A. Mohammadshahi; R. P. Lebret; K. Aberer 

2019-11-03. 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Hong Kong, China, November 3-7, 2019. p. 27-33. DOI : 10.18653/v1/D19-6605.

Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

E. Kakkava; B. Rahmani; N. Borhani; U. Tegin; D. Loterie et al. 

Optical Fiber Technology. 2019-11-01. Vol. 52, p. 101985. DOI : 10.1016/j.yofte.2019.101985.

Learning to Find Unpaired Cross-Spectral Correspondences

S. Jeong; S. Kim; K. Park; K. Sohn 

Ieee Transactions On Image Processing. 2019-11-01. Vol. 28, num. 11, p. 5394-5406. DOI : 10.1109/TIP.2019.2917864.

Review and Benchmarking of Precision-Scalable Multiply-Accumulate Unit Architectures for Embedded Neural-Network Processing

V. Camus; L. Mei; C. Enz; M. Verhelst 

IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS). 2019-10-30. Vol. 9, num. 4, p. 697-711. DOI : 10.1109/JETCAS.2019.2950386.

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

S. Bozorgtabar; M. S. Rad; D. Mahapatra; J-P. Thiran 

2019-10-27. 2019 International Conference on Computer Vision (ICCV 2019), Seoul, South Korea, 27-10, 2019.

Detecting the Unexpected via Image Resynthesis

K. M. Lis; K. K. Nakka; P. Fua; M. Salzmann 

2019-10-27. ICCV 2019 : IEEE International Conference on Computer Vision, Seoul, South Korea, Oct 27, 2019 – Nov 3, 2019 .

What attracts our visual attention? A study on saliency mapping for architectural daylit scenes based on virtual reality data

C. Karmann 

VELUX Daylight Symposium, Paris, France, Octobre 9, 2019.

An Associativity-Agnostic in-Cache Computing Architecture Optimized for Multiplication

M. Rios; W. A. Simon; A. S. J. Levisse; M. Zapater Sancho; D. Atienza Alonso 


Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

R. Bermúdez Chacón; O. Altingövde; C. J. Becker; M. Salzmann; P. Fua 

IEEE Transactions on Medical Imaging. 2019-10-04. DOI : 10.1109/TMI.2019.2946462.

DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

S. Günel; H. Rhodin; D. Morales; J. H. Campagnolo; P. Ramdya et al. 

eLife. 2019-10-04. Vol. 8. DOI : 10.7554/eLife.48571.

Biologically plausible deep learning – But how far can we go with shallow networks?

B. Illing; W. Gerstner; J. Brea 

Neural Networks. 2019-10-01. Vol. 118, p. 90-101. DOI : 10.1016/j.neunet.2019.06.001.

2D MoS2 nanopores: ionic current blockade height for clustering DNA events

A. D. Carral; C. S. Sarap; K. Liu; A. Radenovic; M. Fyta 

2D Materials. 2019-10-01. Vol. 6, num. 4, p. 045011. DOI : 10.1088/2053-1583/ab2c38.

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

F. La Rosa; M. J. Fartaria; A. Abdulkadir; R. Rahmanzadeh; P-J. Lu et al. 

2019-09-10. 35th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS), Stockholm, Sweden, September 11-13, 2019. p. 131-356. DOI : 10.1177/1352458519868078.

Learning online combinatorial stochastic policies with deep reinforcement

T. Stocco; A. Alahi 

2019-09-04. European Association for Research in Transportation (hEART), Budapest, September 4-6, 2019.

Deep learning analysis applied to multi-parametric advanced MRI shows higher myelin content and neurite density in juxtacortical lesions compared to periventricular lesions

P. -J. Lu; R. Rahmanzadeh; R. Galbusera; B. Odry; M. Weigel et al. 

2019-09-01. 35th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS) / 24th Annual Conference of Rehabilitation in MS, Stockholm, SWEDEN, Sep 11-13, 2019. p. 241-242.

Deep learning-based detection of cortical lesions in multiple sclerosis patients with FLAIR, DIR, and MP2RAGE MRI sequences

F. La Rosa; M. J. Fartaria; A. Abdulkadir; R. Rahmanzadeh; P. -J. Lu et al. 

2019-09-01. 35th Congress of the European-Committee-for-Treatment-and-Research-in-Multiple-Sclerosis (ECTRIMS) / 24th Annual Conference of Rehabilitation in MS, Stockholm, SWEDEN, Sep 11-13, 2019. p. 206-207.

Topology classification with deep learning to improve real-time event selection at the LHC

T. Q. Nguyen; D. Weitekamp; D. Anderson; R. Castello; O. Cerri et al. 

Computing and Software for Big Science. 2019-08-31. Vol. 3, p. 12. DOI : 10.1007/s41781-019-0028-1.

Caries Detection with Near-Infrared Transillumination Using Deep Learning

F. Casalegno; T. Newton; R. Daher; M. Abdelaziz; A. Lodi-Rizzini et al. 

Journal of Dental Research. 2019-08-26.  p. 0022034519871884. DOI : 10.1177/0022034519871884.

Metasurface-Based Molecular Biosensing Aided by Artificial Intelligence

A. Tittl; A. John-Herpin; A. Leitis; E. R. Arvelo; H. Altug 

Angewandte Chemie-International Edition. 2019-08-08. DOI : 10.1002/anie.201901443.

Differentiating Parkinson’s disease motor subtypes using automated volume-based morphometry incorporating white matter and deep gray nuclear lesion load

E. Fang; C. N. Ann; B. Marechal; J. X. Lim; S. Y. Z. Tan et al. 

Journal Of Magnetic Resonance Imaging. 2019-07-31. DOI : 10.1002/jmri.26887.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

J. C. Caicedo; J. Roth; A. Goodman; T. Becker; K. W. Karhohs et al. 

Cytometry Part A. 2019-07-16. DOI : 10.1002/cyto.a.23863.

Benefiting from Multitask Learning to Improve Single Image Super-Resolution

M. S. Rad; B. Bozorgtabar; C. Musat; U-V. Marti; M. Basler et al. 

Neurocomputing. 2019-07-14. 

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

M. Geiger; S. Spigler; S. d’Ascoli; L. Sagun; M. Baity-Jesi et al. 

Physical Review E. 2019-07-11. Vol. 100, num. 1, p. 012115. DOI : 10.1103/PhysRevE.100.012115.

A deep learning approach to Cadastral Computing

S. Ares Oliveira; I. di Lenardo; B. Tourenc; F. Kaplan 

2019-07-11. Digital Humanities Conference,, Utrecht, Netherlands, July 8-12, 2019.

Mirror, Mirror, on the Wall, Who’s Got the Clearest Image of Them All?—A Tailored Approach to Single Image Reflection Removal

D. Heydecker; G. Maierhofer; A. I. Aviles-Rivera; F. Qingnan; D. Chen et al. 

IEEE Transactions on Image Processing. 2019-07-01. Vol. 28, num. 12, p. 6185-6197. DOI : 10.1109/TIP.2019.2923559.

A Text Mining Pipeline Using Active and Deep Learning Aimed at Curating Information in Computational Neuroscience

M. Shardlow; M. Ju; M. Li; C. O’Reilly; E. Iavarone et al. 

Neuroinformatics. 2019-07-01. Vol. 17, num. 3, p. 391-406. DOI : 10.1007/s12021-018-9404-y.

Stochastic Zeroth-Order Optimisation Algorithms with Variance Reduction

A. Ajalloeian 


Geometric Deep Learning for Volumetric Computational Fluid Dynamics

L. Zampieri 


Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs

F. Gusset 


Neural Scene Decomposition for Multi-Person Motion Capture

H. Rhodin; V. Constantin; I. Katircioglu; M. Salzmann; P. Fua 

2019-06-20. Computer Vision and Patter Recognition (CVPR).

Context-Aware Crowd Counting

W. Liu; M. Salzmann; P. Fua 

2019-06-20. Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, June 16-20, 2019.

A comparison of model-parallel training methods for deep learning

P. Kang 


MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation

L. Bertoni; S. Kreiss; A. Alahi 

2019-06-07. International Conference on Computer Vision (ICCV), Seoul, Korea, October 27- November 2, 2019 .

Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends

A. Hirschmann; J. Cyriac; B. Stieltjes; T. Kober; J. Richiardi et al. 

Seminars In Musculoskeletal Radiology. 2019-06-01. Vol. 23, num. 3, p. 304-311. DOI : 10.1055/s-0039-1684024.

Design of an Always-On Deep Neural Network-Based 1-mu W Voice Activity Detector Aided With a Customized Software Model for Analog Feature Extraction

M. Yang; C-H. Yeh; Y. Zhou; J. P. Cerqueira; A. A. Lazar et al. 

Ieee Journal Of Solid-State Circuits. 2019-06-01. Vol. 54, num. 6, p. 1764-1777. DOI : 10.1109/JSSC.2019.2894360.

Improving speech embedding using crossmodal transfer learning with audio-visual data

N. Le; J-M. Odobez 

Multimedia Tools and Applications. 2019-06-01. Vol. 78, num. 11, p. 15681-15704. DOI : 10.1007/s11042-018-6992-3.

Learning from droplet flows in microfluidic channels using deep neural networks

P. Hadikhani; N. Borhanil; S. M. H. Hashemi; D. Psaltis 

Scientific Reports. 2019-05-31. Vol. 9, p. 8114. DOI : 10.1038/s41598-019-44556-x.

Deep Drone Racing: From Simulation to Reality with Domain Randomization

A. Loquercio; E. Kaufmann; R. Ranftl; A. Dosovitskiy; V. Koltun et al. 


Enhancing subwavelength image recognition with resonant metamaterial lenses

B. Orazbayev; R. Fleury 

URSI Commission B International Symposium on Electromagnetic Theory (EMTS 2019), San Diego, United Stated, 27-31 May 2019.

Feed-forwards meet recurrent networks in vehicle trajectory prediction

M. Bahari; A. Alahi 

2019-05-15. Swiss Transport Research Conference (STRC).

Using Photorealistic Face Synthesis and Domain Adaptation to Improve Facial Expression Analysis

B. Bozorgtabar; M. S. Rad; H. K. Ekenel; J-P. Thiran 

2019-05-14. The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), Lille, France, May 14 -18, 2019. p. 230-237.

Audio Feature Extraction with Convolutional Neural Autoencoders with Application to Voice Conversion

G. Elhami; R. M. Weber 

2019-05-12. May 12-17, 2019.

Local SGD Converges Fast and Communicates Little

S. U. Stich 

2019-05-06. ICLR 2019 – International Conference on Learning Representations, New Orleans, USA, May 6-9, 2019.

Incremental Learning Meets Reduced Precision Networks

Y. Hu; T. Delbruck; S-C. Liu 

2019-05-01. 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, May 26-28, 2019. p. 1-5. DOI : 10.1109/ISCAS.2019.8702541.

Low-rank and sparse subspace modeling of speech for DNN based acoustic modeling

P. Dighe; A. Asaei; H. Bourlard 

Speech Communication. 2019-05-01. Vol. 109, p. 34-45. DOI : 10.1016/j.specom.2019.03.004.

MATHICSE Technical Report: Constraint-Aware Neural Networks for Riemann Problems

J. Magiera; D. Ray; J. S. Hesthaven; C. Rohde 


Informative sample generation using class aware generative adversarial networks for classification of chest Xrays

B. Bozorgtabar; D. Mahapatra; H. von Teng; A. Pollinger; L. Ebner et al. 

Computer Vision and Image Understanding (CVIU). 2019-04-17. 

Neural network training for cross-protocol radiomic feature standardization in computed tomography

V. Andrearczyk; A. Depeursinge; H. Muller 

Journal Of Medical Imaging. 2019-04-01. Vol. 6, num. 2, p. 024008. DOI : 10.1117/1.JMI.6.2.024008.

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

L. Ge; H. Liang; J. Yuan; D. Thalmann 

Ieee Transactions On Pattern Analysis And Machine Intelligence. 2019-04-01. Vol. 41, num. 4, p. 956-970. DOI : 10.1109/TPAMI.2018.2827052.

DeepSphere: Efficient spherical convolutional neural network with HEALPix sampling for cosmological applications

N. Perraudin; M. Defferrard; T. Kacprzak; R. Sgier 

Astronomy And Computing. 2019-04-01. Vol. 27, p. 130-146. DOI : 10.1016/j.ascom.2019.03.004.

Learning Vision-Based Quadrotor Control in User Proximity

D. Mantegazza; J. Guzzi; L. M. Gambardella; A. Giusti 

2019-03-25. 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), Daegu, Korea (South), March 11-14, 2019. p. 369-369. DOI : 10.1109/HRI.2019.8673022.

Survey of Precision-Scalable Multiply-Accumulate Units for Neural-Network Processing

V. Camus; C. Enz; M. Verhelst 


A computer vision system for deep learning-based detection of patient mobilization activities in the ICU

S. Yeung; F. Rinaldo; J. Jopling; B. Liu; R. Mehra et al. 

Npj Digital Medicine. 2019-03-01. Vol. 2, p. 11. DOI : 10.1038/s41746-019-0087-z.

Biologically plausible deep learning – but how far can we go with shallow networks?

B. Illing; W. Gerstner; J. Brea 


Real-Time Wide-Baseline Place Recognition Using Depth Completion

F. Maffra; L. Teixeira; Z. Chen; M. Chli 

IEEE Robotics and Automation Letters. 2019-01-29. Vol. 4, num. 2, p. 1525-1532. DOI : 10.1109/LRA.2019.2895826.

Challenges and implemented technologies used in autonomous drone racing

H. Moon; J. Martinez-Carranza; T. Cieslewski; M. Faessler; D. Falanga et al. 

Intelligent Service Robotics. 2019-01-24. Vol. 12, num. 2, p. 137-148. DOI : 10.1007/s11370-018-00271-6.

Modular Sensor Fusion for Semantic Segmentation

H. Blum; A. Gawel; R. Siegwart; C. Cadena 

2019-01-07. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, October 1-5, 2018. p. 3670-3677. DOI : 10.1109/IROS.2018.8593786.

Multi-agent reinforcement learning for adaptive demand response in smart cities

J. Vázquez-Canteli; T. Detjeen; G. Henze; J. Kämpf; Z. Nagy 

2019. DOI : 10.1088/1742-6596/1343/1/012058.

CityLearn v1.0: An OpenAI Gym Environment for Demand Response with Deep Reinforcement Learning

J. Vázquez-Canteli; J. Kämpf; G. Henze; Z. Nagy 

2019. 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, New-York, USA, p. 356-357. DOI : 10.1145/3360322.3360998.

Learning an event sequence embedding for event-based deep stereo

S. Tulyakov; F. Fleuret; M. Kiefel; P. Gehler; M. Hirsch 

2019. Proceedings of the IEEE International Conference on Computer Vision.

Self-attention for Speech Emotion Recognition

L. Tarantino; P. N. Garner; A. Lazaridis 

2019. Proc. Interspeech 2019. DOI : 10.21437/Interspeech.2019-2822.


P. Motlicek; H. Hermansky; S. Madikeri; A. Prasad; S. Ganapathy 

2019. Universita Degli Studi Firenze – 11th International workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, Firenze, Italy,

PassGAN: A Deep Learning Approach for Password Guessing

B. Hitaj; P. Gasti; G. Ateniese; F. Perez-Cruz 

2019-01-01. 17th International Conference on Applied Cryptography and Network Security (ACNS), Bogota, Colombia, Jun 05-07, 2019. p. 217-237. DOI : 10.1007/978-3-030-21568-2_11.

Architectural Sampling: A Formal Basis for Machine-Learnable Architecture

I. C. B. Koh / J. Huang (Dir.)  

Lausanne, EPFL, 2019. 

Mobile Robotic Painting of Texture

M. El Helou; S. Mandt; A. Krause; P. Beardsley 

2019-01-01. International Conference on Robotics and Automation (ICRA), Montreal, CANADA, May 20-24, 2019. p. 640-647.

Byzantine tolerant gradient descent for distributed machine learning with adversaries

P. Blanchard; E. M. El Mhamdi; R. Guerraoui; J. Stainer 



Trustworthy speaker recognition with minimal prior knowledge using neural networks

H. Muckenhirn / H. Bourlard; M. Magimai Doss (Dir.)  

Lausanne, EPFL, 2019. 

On Problem Formulation, Efficient Modeling and Deep Neural Networks for High-Quality Ultrasound Imaging

D. Perdios; A. Besson; F. Martinez; M. Vonlanthen; M. Arditi et al. 

2019-01-01. 53rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, Mar 20-22, 2019.