Recherche

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.

2021

On the effectiveness of adversarial training against common corruptions

K. Kireev; M. Andriushchenko; N. Flammarion 

2021-03-03

On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines

M. Mosbach; M. Andriushchenko; D. Klakow 

2021. 9th International Conference on Learning Representations, Virtual, May 4-8, 2021.

Datasets and Models for Historical Newspaper Article Segmentation

R. Barman; M. Ehrmann; S. Clematide; S. Ares Oliveira 

2021.

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

R. Barman; M. Ehrmann; S. Clematide; S. Ares Oliveira; F. Kaplan 

Journal of Data Mining & Digital Humanities. 2021. Vol. 2021, num. Special Issue on HistoInformatics: Computational Approaches to History, p. 1-26. DOI : 10.5281/zenodo.4065271.

2020

Probabilistic Deep Learning on Spheres for Weather/Climate Applications

Y. Y. Haddad 

2020-12-16.

Probabilistic Deep Learning on Spheres for Weather/Climate Applications

W. Feng 

2020-12-16.

Detecting 32 Pedestrian Attributes for Autonomous Vehicles

T. Mordan; M. Cord; P. Pérez; A. Alahi 

2020-12-04

AIDE: Accelerating image‐based ecological surveys with interactive machine learning

B. Kellenberger; D. Tuia; D. Morris 

Methods in Ecology and Evolution. 2020-12-03. Vol. 11, num. 12, p. 1716-1727. DOI : 10.1111/2041-210X.13489.

RSVQA: Visual Question Answering for Remote Sensing Data

S. Lobry; D. Marcos; J. Murray; D. Tuia 

IEEE Transactions on Geoscience and Remote Sensing. 2020-11-26. Vol. 58, num. 12, p. 8555-8566. DOI : 10.1109/TGRS.2020.2988782.

Brain micro-vasculature imaging: An unsupervised deep learning algorithm for segmenting mouse brain volume probed by high-resolution phase-contrast X-ray tomography

A. Patera; A. G. Zippo; A. Bonnin; M. Stampanoni; G. E. M. Biella 

International Journal Of Imaging Systems And Technology. 2020-11-15. DOI : 10.1002/ima.22520.

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. 

2020-11-05

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

W. Wang; X. Alameda-Pineda; D. Xu; E. Ricci; N. Sebe 

Ieee Transactions On Multimedia. 2020-11-01. Vol. 22, num. 11, p. 2808-2819. DOI : 10.1109/TMM.2019.2963621.

Disentangling feature and lazy training in deep neural networks

M. Geiger; S. Spigler; A. Jacot; M. Wyart 

Journal Of Statistical Mechanics-Theory And Experiment. 2020-11-01. Vol. 2020, num. 11, p. 113301. DOI : 10.1088/1742-5468/abc4de.

Extended Overview of CLEF HIPE 2020: Named Entity Processing on Historical Newspapers

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

2020-10-21. 11th Conference and Labs of the Evaluation Forum (CLEF 2020), [Online event], 22-25 September, 2020. DOI : 10.5281/zenodo.4117566.

RobustBench: a standardized adversarial robustness benchmark

F. Croce; M. Andriushchenko; V. Sehwag; N. Flammarion; M. Chiang et al. 

2020-10-19

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

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

2020-10-19. 

Design Patterns for Resource-Constrained Automated Deep-Learning Methods

L. Tuggener; M. Amirian; F. Benites; P. von Däniken; P. Gupta et al. 

AI. 2020-10-06. Vol. 1, num. 4, p. 510-538. DOI : 10.3390/ai1040031.

Deep learning-based reduced order models in cardiac electrophysiology

S. Fresca; A. Manzoni; L. Dede; A. Quarteroni 

Plos One. 2020-10-01. Vol. 15, num. 10, p. e0239416. DOI : 10.1371/journal.pone.0239416.

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

A. Mathis; S. Schneider; J. Lauer; M. W. Mathis 

Neuron. 2020-10. Vol. 108, num. 1, p. 44-65. DOI : 10.1016/j.neuron.2020.09.017.

DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation

A. Carlier; M. Danelljan; A. Alahi; R. Timofte 

2020-09-29. NeurIPS 2020 34th Conference on Neural Information Processing Systems, Vancouver, Canada, December 6-12, 2020.

A deep learning framework for matching of SAR and optical imagery

L. H. Hughes; D. Marcos; S. Lobry; D. Tuia; M. Schmitt 

ISPRS Journal of Photogrammetry and Remote Sensing. 2020-09-23. Vol. 169, p. 166-179. DOI : 10.1016/j.isprsjprs.2020.09.012.

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.

Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals

J. Bonnemain; L. Pegolotti; L. Liaudet; S. Deparis 

Frontiers In Physiology. 2020-09-11. Vol. 11, p. 1086. DOI : 10.3389/fphys.2020.01086.

Short-term energy use prediction of solar-assisted water heating system: Application case of combined attention-based LSTM and time-series decomposition

A. Heidari; D. Khovalyg 

Solar Energy. 2020-09-01. Vol. 207, p. 626-639. DOI : 10.1016/j.solener.2020.07.008.

Upgrading the Newsroom: An Automated Image Selection System for News Articles

F. Liu; R. Lebret; D. Orel; P. Sordet; K. Aberer 

Acm Transactions On Multimedia Computing Communications And Applications. 2020-09-01. Vol. 16, num. 3, p. 81. DOI : 10.1145/3396520.

Assessing Public Opinion on CRISPR-Cas9: Combining Crowdsourcing and Deep Learning

M. Muller; M. Schneider; M. Salathe; E. Vayena 

Journal Of Medical Internet Research. 2020-08-31. Vol. 22, num. 8, p. e17830. DOI : 10.2196/17830.

Precise Hand Finger Width Estimation via RGB-D Data

M. Nobar 

2020-08-31.

Square Attack: a query-efficient black-box adversarial attack via random search

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

2020-08-28. European Conference on Computer Vision (ECCV 2020), [Online], August 23-28, 2020.

Comprehensive assessment of image compression algorithms

M. Testolina; E. Upenik; T. Ebrahimi 

2020-08-24. SPIE Optics + Photonics Digital Forum, Applications of Digital Image Processing XLIII, [Online only], 24-28 August 2020. DOI : 10.1117/12.2569255.

Musical Source Separation

A. Mocanu 

2020-08-14.

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.

Real-time, low-latency closed-loop feedback using markerless posture tracking

G. Kane; G. Lopes; J. L. Saunders; A. Mathis; M. W. Mathis 

2020-08-05

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. DOI : 10.1016/j.neucom.2019.07.107.

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. DOI : 10.1109/TPAMI.2020.3010886.

Human Trajectory Forecasting in Crowds: A Deep Learning Perspective

P. Kothari; S. Kreiss; A. Alahi 

2020-07-07

Understanding and Improving Fast Adversarial Training

M. Andriushchenko; N. Flammarion 

2020-07-06. Advances In Neural Information Processing Systems 33 (NeurIPS 2020), [Online], December 2020.

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 

2020-06-23

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

S. Roquette 

2020-06-19.

On the Experimental Transferability of Spectral Graph Convolutional Networks

A. Nilsson 

2020-06-19.

Geometric deep learning for medium-range weather prediction

I. Llorens Jover 

2020-06-19.

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.

DISK: learning local features with policy gradient

M. J. Tyszkiewicz; P. Fua; E. Trulls 

2020-06-01. Neural Information Processing Systems, Vancouver, Canada (online), December 6-12, 2020.

ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture

S. Kiciroglu; H. Rhodin; S. N. Sinha; M. Salzmann; P. Fua 

2020-06. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, Washington, USA, June 14-19, 2020.

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/j.jcp.2020.109345.

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.

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.

(Geometry Aware) Deep Learning-based Omnidirectional Image Compression

Y. Sepehri 

2020-02-06.

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 

2020-02-03.

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/j.media.2019.101590.

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.

Deep learning for automated detection of Drosophila suzukii : potential for UAV ‐based monitoring

P. P. Roosjen; B. Kellenberger; L. Kooistra; D. R. Green; J. Fahrentrapp 

Pest Management Science. 2020. Vol. 76, num. 9, p. 2994-3002. DOI : 10.1002/ps.5845.

ResLogit: A residual neural network logit model for data-driven choice modelling

M. Wong; B. Farooq 

2020

Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability

G. Camps-Valls; M. Reichstein; X. Zhu; D. Tuia 

2020. IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI (held online), July 2020. p. 3979-3982. DOI : 10.1109/IGARSS39084.2020.9323558.

Interpretable Scenicness from Sentinel-2 Imagery

A. Levering; D. Marcos; S. Lobry; D. Tuia 

2020. IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA(held online), Septembre, 2020. p. 3983-3986. DOI : 10.1109/IGARSS39084.2020.9323706.

Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

Y. Hua; S. Lobry; L. Mou; D. Tuia; X. X. Zhu 

2020. IGARSS 2020 – 2020 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA (held online), Septembre, 2020. p. 525-528. DOI : 10.1109/IGARSS39084.2020.9324069.

Better Generic Objects Counting When Asking Questions to Images: A Multitask approach for Remote Sensing Visual Question Answering

S. Lobry; D. Marcos; B. Kellenberger; D. Tuia 

2020. ISPRS congress, Online conference, September 2020. p. 1021-1027. DOI : 10.5194/isprs-annals-V-2-2020-1021-2020.

Contextual semantic interpretability

D. Marcos; S. Lobry; R. Fong; N. Courty; R. Flamary et al. 

2020. Asian Conference on Computer Vision (ACCV), Kyoto, Japan (held online), November 30, 2020.

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

S. Srivastava; J. E. Vargas Muñoz; S. Lobry; D. Tuia 

International Journal of Geographical Information Science. 2020. Vol. 34, num. 6, p. 1117-1136. DOI : 10.1080/13658816.2018.1542698.

Knowledge, Machine Learning and Atrial Fibrillation: More Ingredients for a Tastier Cocktail

T. Teijeiro 

2020. 2020 Computing in Cardiology Conference (CinC), Rimini, Italy, September 13th-16th, 2020. DOI : 10.22489/CinC.2020.476.

CLEF-HIPE-2020 – Shared Task Participation Guidelines

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

2020

Deep Learning the Hohenberg-Kohn Maps of Density Functional Theory

J. R. Moreno; G. Carleo; A. Georges 

Physical Review Letters. 2020. Vol. 125, num. 7. DOI : 10.1103/PhysRevLett.125.076402.

The Learnability of the Grammar of Jazz: Bayesian Inference of Hierarchical Structures in Harmony

D. Harasim / M. A. Rohrmeier; T. J. O’Donnell (Dir.)  

Lausanne, EPFL, 2020. 

Deep learning tools for the measurement of animal behavior in neuroscience

M. W. Mathis; A. Mathis 

Current Opinion in Neurobiology. 2020. Vol. 60, p. 1-11. DOI : 10.1016/j.conb.2019.10.008.

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks.

K. Pitas / P. Vandergheynst (Dir.)  

Lausanne, EPFL, 2020. 

Automatic detection of hand hygiene using computer vision technology

A. Singh; A. Haque; A. Alahi; s. Yeung; m. Guo et al. 

Journal of the American Medical Informatics Association. 2020. Vol. 27, num. 8, p. 1316-1320. DOI : 10.1093/jamia/ocaa115.

Removing Structured Noise With Self-Supervised Blind-Spot Networks

C. Broaddus; A. Krull; M. Weigert; U. Schmidt; G. Myers 

2020-01-01. IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa, IA, Apr 03-07, 2020. p. 159-163.

UCLID-Net: Single View Reconstruction in Object Space

B. Guillard; E. Remelli; P. Fua 

2020. 34th Conference on Neural Information Processing Systems, Virtual, December 6-12, 2020.

Image Restoration using Plug-and-Play CNN MAP Denoisers

S. Bigdeli; D. Honzatko; S. Suesstrunk; L. A. Dunbar 

2020-01-01. 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP), Valletta, MALTA, Feb 27-29, 2020. p. 85-92. DOI : 10.5220/0008990700850092.

Learning approaches to high-fidelity optical diffraction tomography

J. Lim / D. Psaltis (Dir.)  

Lausanne, EPFL, 2020. 

Image Compression and Quality Assessment using Convolutional Neural Networks

P. Akyazi / T. Ebrahimi (Dir.)  

Lausanne, EPFL, 2020. 

Data-Aware Privacy-Preserving Machine Learning

A. Triastcyn / B. Faltings (Dir.)  

Lausanne, EPFL, 2020. 

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

H. Gupta / M. Unser (Dir.)  

Lausanne, EPFL, 2020. 

Wavefront shaping and deep learning in fiber endoscopy

E. Kakkava / D. Psaltis (Dir.)  

Lausanne, EPFL, 2020. 

Truthful, Transparent and Fair Data Collection Mechanisms

N. Goel / B. Faltings (Dir.)  

Lausanne, EPFL, 2020.