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.


Overview of CLEF HIPE 2020: Named Entity Recognition and Linking on Historical Newspapers

M. Ehrmann; M. Romanello; A. Flückiger; S. Clematide 

2020-09-15. 11th International Conference of the CLEF Association – CLEF 2020, Thessaloniki, Greece, September 22–25, 2020. p. 288–310. DOI : 10.1007/978-3-030-58219-7_21.

Precise Hand Finger Width Estimation via RGB-D Data

M. Nobar 


Musical Source Separation

A. Mocanu 


Far-Field Subwavelength Acoustic Imaging by Deep Learning

B. Orazbayev; R. Fleury 

Physical Review X. 2020-08-07. Vol. 10, p. 031029. DOI : 10.1103/PhysRevX.10.031029.

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

B. Zhu; M. Farivar; M. Shoaran 

IEEE Transactions on Biomedical Circuits and Systems. 2020-08-01. Vol. 14, num. 4, p. 692-704. DOI : 10.1109/TBCAS.2020.3004544.

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

S. Tortora; S. Ghidoni; C. Chisari; S. Micera; F. Artoni 

Journal Of Neural Engineering. 2020-08-01. Vol. 17, num. 4, p. 046011. DOI : 10.1088/1741-2552/ab9842.

Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets

E. Taghizadeh; O. Truffer; F. Becce; S. Eminian; S. Gidoin et al. 

European Radiology. 2020-07-22. DOI : 10.1007/s00330-020-07070-7.

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. 2020-07-20. Vol. 398, num. 398, p. 304-313.

Redundant features can hurt robustness to distributions shift

G. Ortiz Jimenez; A. Modas; S. M. Moosavi Dezfooli; P. Frossard 

Uncertainty & Robustness in Deep Learning Workshop (ICML 2020).

GarNet++: Improving Fast and Accurate Static 3D Cloth Draping by Curvature Loss

E. Gündogdu; V. Constantin; S. Parashar; A. Seifoddini; M. Dang et al. 

IEEE Transactions On Pattern Analysis And Machine Intelligence (PAMI). 2020-07-14. 

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

P. Kothari; S. Kreiss; A. Alahi 


Understanding and Improving Fast Adversarial Training

M. Andriushchenko; N. Flammarion 


Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments

D. Morone; A. Marazza; T. J. Bergmann; M. Molinari 

Molecular Biology Of The Cell. 2020-07-01. Vol. 31, num. 14, p. 1512-1524. DOI : 10.1091/mbc.E20-04-0269.

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE

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

NeuroImage: Clinical. 2020-06-30. Vol. 27, p. 102335. DOI : 10.1016/j.nicl.2020.102335.

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

F. Croce; M. Andriushchenko; N. Singh; N. Flammarion; M. Hein 


Detecting solar rooftop photovoltaic panels in aerial images using neural networks: a transfer learning approach

S. Roquette 


On the Experimental Transferability of Spectral Graph Convolutional Networks

A. Nilsson 


Geometric deep learning for medium-range weather prediction

I. Llorens Jover 


WatchNet plus plus : efficient and accurate depth-based network for detecting people attacks and intrusion

M. Villamizar; A. Martinez-Gonzalez; O. Canevet; J. -M. Odobez 

Machine Vision And Applications. 2020-06-17. Vol. 31, num. 6, p. 41. DOI : 10.1007/s00138-020-01089-y.

Shape Reconstruction by Learning Differentiable Surface Representations

J. Bednarík; S. Parashar; E. Gündogdu; M. Salzmann; P. Fua 

2020-06-14. 2020 CVPR – Computer Vision and Pattern Recognition, Seattle, USA, June 14-16, 2020.

Editorial: Computational Pathology

B. Bozorgtabar; D. Mahapatra; I. Zlobec; T. T. Rau; J-P. Thiran 

Frontiers In Medicine. 2020-06-09. Vol. 7, p. 245. DOI : 10.3389/fmed.2020.00245.

Joint Segmentation and Path Classification of Curvilinear Structures

A. Mosinska; M. Kozinski; P. Fua 

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2020-06-01. Vol. 42, num. 6, p. 1515-1521. DOI : 10.1109/TPAMI.2019.2921327.

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.

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

E. Kakkava; N. Borhani; B. Rahmani; U. Teğin; C. Moser et al. 

Applied Sciences. 2020-05-30. Vol. 10, num. 11, p. 3816. DOI : 10.3390/app10113816.

Accurate deep neural network inference using computational phase-change memory

V. Joshi; M. Le Gallo; S. Haefeli; I. Boybat; S. R. Nandakumar et al. 

Nature Communications. 2020-05-18. Vol. 11, num. 1. DOI : 10.1038/s41467-020-16108-9.

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.

Constraint-aware neural networks for Riemann problems

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

Journal Of Computational Physics. 2020-05-15. Vol. 409, p. 109345. DOI : 10.1016/

Multi-View Shape Estimation of Transparent Containers

A. Xompero; R. Sanchez-Matilla; A. Modas; P. Frossard; A. Cavallaro 

2020-05-08. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), [Online event], May 4-8, 2020. DOI : 10.1109/ICASSP40776.2020.9054112.

Optimization for Reinforcement Learning: From a single agent to cooperative agents

D. Lee; N. He; P. Kamalaruban; V. Cevher 

Ieee Signal Processing Magazine. 2020-05-01. Vol. 37, num. 3, p. 123-135. DOI : 10.1109/MSP.2020.2976000.

Unsupervised Stereo Matching Using Confidential Correspondence Consistency

S. Joung; S. Kim; K. Park; K. Sohn 

Ieee Transactions On Intelligent Transportation Systems. 2020-05-01. Vol. 21, num. 5, p. 2190-2203. DOI : 10.1109/TITS.2019.2917538.

Introducing the CLEF 2020 HIPE Shared Task: Named Entity Recognition and Linking on Historical Newspapers

M. Ehrmann; M. Romanello; S. Bircher; S. Clematide 

2020-04-08. ECIR 2020 : 42nd European Conference on Information Retrieval, Lisbon, Portugal, April 14-17, 2020. p. 524-532. DOI : 10.1007/978-3-030-45442-5_68.

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.

Automated Essay Scoring in Foreign Language Students Based on Deep Contextualised Word Representations

B. Ranković; S. Smirnow; M. Jaggi; M. J. Tomasik 

2020-03-23. LAK20 – 10th International Conference on Learning Analytics & Knowledge, Mars 23, 2020.

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.

Classification of tokamak plasma confinement states with convolutional recurrent neural networks

F. Matos; V. Menkovski; F. Felici; A. Pau; F. Jenko 

Nuclear Fusion. 2020-03-01. Vol. 60, num. 3, p. 036022. DOI : 10.1088/1741-4326/ab6c7a.

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

U. Tegin; B. Rahmani; E. Kakkava; N. Borhani; C. Moser et al. 

Apl Photonics. 2020-03-01. Vol. 5, num. 3, p. 030804. DOI : 10.1063/1.5138131.

An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

S. Ali; F. Zhou; B. Braden; A. Bailey; S. Yang et al. 

Scientific Reports. 2020-02-17. Vol. 10, num. 1, p. 2748. DOI : 10.1038/s41598-020-59413-5.

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. p. 4948-4956. DOI : 10.1609/aaai.v34i04.5933.

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.

Deep-learning projector for optical diffraction tomography

F. Yang; T-A. Pham; H. Gupta; M. Unser; J. Ma 

Optics Express. 2020-02-03. Vol. 28, num. 3, p. 3905-3921. DOI : 10.1364/OE.381413.

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

C. Capelo 


Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

M. Kozinski; A. Mosinska; M. Salzmann; P. Fua 

Medical Image Analysis. 2020-02-01. Vol. 60, p. 101590. DOI : 10.1016/

Scaling description of generalization with number of parameters in deep learning

M. Geiger; A. Jacot; S. Spigler; F. Gabriel; L. Sagun et al. 

Journal Of Statistical Mechanics-Theory And Experiment. 2020-02-01. Vol. 2020, num. 2, p. 023401. DOI : 10.1088/1742-5468/ab633c.

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.

Methods for strong gravitational lens detection and analysis using machine learning and high performance computing

C. E. R. Schäfer / J-P. R. Kneib (Dir.)  

Lausanne, EPFL, 2020. 

Comparison of crack segmentation using digital image correlation measurements and deep learning

A. Rezaie; R. Achanta; M. Godio; K. Beyer 

Construction and Building Materials. 2020. Vol. 261, p. 1-12, 120474. DOI : 10.1016/j.conbuildmat.2020.120474.

Reconstruction Methods for Cryo-Electron Microscopy: From Model-based to Data-driven

L. Donati / M. Unser; D. Sage (Dir.)  

Lausanne, EPFL, 2020. 

Data Structures and Algorithms for Logic Synthesis in Advanced Technologies

E. Testa / G. De Micheli; M. Soeken (Dir.)  

Lausanne, EPFL, 2020. 

Towards neural network approaches for point cloud compression

E. Alexiou; K. Tung; T. Ebrahimi 

2020. SPIE Optical Engineering + Applications, Online, August 24-28, 2020. p. 1151008. DOI : 10.1117/12.2569115.

W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

R. Zhou; M. El Helou; D. Sage; T. Laroche; A. Seitz et al. 

2020. European Conference on Computer Vision Workshops 2020, Glasgow, United Kingdom, August 23-28, 2020.

Single Image Deraining Using Time-Lapse Data

J. Cho; S. Kim; D. Min; K. Sohn 

Ieee Transactions On Image Processing. 2020-01-01. Vol. 29, p. 7274-7289. DOI : 10.1109/TIP.2020.3000612.

Towards Real-World Super-Resolution using Deep Neural Networks

R. Zhou / S. Süsstrunk (Dir.)  

Lausanne, EPFL, 2020. 

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

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

2020. 37th International Conference on Machine Learning, Vienna, Austria.

Redundant features can hurt robustness to distribution shift

G. Ortiz Jimenez; A. Modas; S. M. Moosavi Dezfooli; P. Frossard 

2020. ICML 2020 Workshop on Uncertainty & Robustness in Deep Learning, [Online event], July 17, 2020.

Deep Generative Models and Applications

T. Chavdarova / F. Fleuret (Dir.)  

Lausanne, EPFL, 2020. 

Neural Network Based End-to-End Query by Example Spoken Term Detection

D. Ram; L. Miculicich; H. Bourlard 

Ieee-Acm Transactions On Audio Speech And Language Processing. 2020-01-01. Vol. 28, p. 1416-1427. DOI : 10.1109/TASLP.2020.2988788.

Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data

P. U. Wickramasinghe; E. Remelli; G. Knott; P. Fua 

2020. 23rd International Conference On Medical Image Computing & Computer Assisted Intervention, Lima, Peru, 4-8 OCTOBER 2020.

Dynamic Model Pruning with Feedback

T. Lin; S. U. Stich; L. F. Barba Flores; D. Dmitriev; M. Jaggi 

2020. 8th International Conference on Learning Representations (ICLR), Virtual Conference, Formerly Addis Ababa, Ethiopia, April 26-30, 2020.

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

R. Sahu; S. R. Dash; L. A. Cacha; R. R. Poznanski; S. Parida 

Journal of Integrative Neuroscience. 2020. Vol. 19, num. 1, p. 1-9. DOI : 10.31083/j.jin.2020.01.24.

Trustworthy Face Recognition: Improving Generalization of Deep Face Presentation Attack Detection

A. Mohammadi / H. Bourlard; S. Marcel (Dir.)  

Lausanne, EPFL, 2020. 


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


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


Enhancing discrete choice models with representation learning

B. Sifringer; V. Lurkin; A. Alahi 

Transportation Research Part B: Methodological. 2020. Vol. 140, p. 236-261. DOI : 10.1016/j.trb.2020.08.006.


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. 

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. p. 244-249.

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.

Detecting the Unexpected via Image Resynthesis

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

2019-10-27. IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, SOUTH KOREA, Oct 27-Nov 02, 2019. p. 2152-2161. DOI : 10.1109/ICCV.2019.00224.

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.

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.

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.

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

M. Witman; D. Gidon; D. B. Graves; B. Smit; A. Mesbah 

Plasma Sources Science and Technology. 2019-09-24. Vol. 28, num. 9, p. 095019. DOI : 10.1088/1361-6595/ab3c15.

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.