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.

2019

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.

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.

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 .

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 

2019-10-09. 

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.

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.

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 / S. U. Stich; M. Jaggi (Dir.)  

EPFL, 2019-06-21. 

Geometric Deep Learning for Volumetric Computational Fluid Dynamics

L. Zampieri 

2019-06-21.

Spherical Convolutionnal Neural Networks: Empirical Analysis of SCNNs

F. Gusset 

2019-06-21.

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.

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.

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. 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.

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 

2019-04-28

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.

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

V. Camus; C. Enz; M. Verhelst 

2019-03-18. 

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 

2019-03-01. 

Collaborative Sampling in Generative Adversarial Networks

Y. Liu; P. A. Kothari; A. Alahi 

2019-02-11. 

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.

UAV-Based Situational Awareness System Using Deep Learning

R. Geraldes; A. Goncalves; T. Lai; M. Villerabel; W. Deng et al. 

IEEE Access. 2019-01-01. Vol. 7, p. 122583-122594. DOI : 10.1109/ACCESS.2019.2938249.

Deep learning assisted image transmission in multimode fibers

B. Rahmani; D. Loterie; G. Konstantinou; D. Psaltis; C. Moser 

2019-01-01. Conference on Adaptive Optics and Wavefront Control for Biological Systems V, held at SPIE BiOS, San Francisco, CA, Feb 03-04, 2019. p. 108860N. DOI : 10.1117/12.2508383.

Deep Spline Networks With Control Of Lipschitz Regularity

S. Aziznejad; M. Unser 

2019-01-01. 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, ENGLAND, May 12-17, 2019. p. 3242-3246.

Impact of Memory Voltage Scaling on Accuracy and Resilience of Deep Learning Based Edge Devices

B. W. Denkinger; F. Ponzina; S. S. Basu; A. Bonetti; S. Balási et al. 

IEEE Design & Test. 2019. 

Remote Sensing meets Deep Learning: Exploiting Spatio-Temporal-Spectral Satellite Images for Early Wildfire Detection

T. C. Phan; T. T. Nguyen 

2019

Abstract Text Summarization: A Low Resource Challenge

S. Parida; P. Motlicek 

2019. na. p. 5.

Understanding and Visualizing Raw Waveform-based CNNs

H. Muckenhirn; V. Abrol; M. Magimai.-Doss; S. Marcel 

2019. Proceedings of Interspeech.

Tampered Speaker Inconsistency Detection with Phonetically Aware Audio-visual Features

P. Korshunov; M. Halstead; D. Castan; M. Graciarena; M. McLaren et al. 

2019. International Conference on Machine Learning.

Processing Megapixel Images with Deep Attention-Sampling Models

A. Katharopoulos; F. Fleuret 

2019. Proceedings of International Conference on Machine Learning.

Zero-Learning Fast Medical Image Fusion

F. Lahoud; S. Süsstrunk 

2019. 22nd International Conference on Information Fusion (FUSION 2019), Ottawa, Canada, July 2-5, 2019.

[Re] Meta-learning with differentiable closed-form solvers

A. Devos; S. Chatel; M. Grossglauser 

The ReScience journal C. 2019. Vol. 5, num. 2, p. #1.

Overcoming Multi-model Forgetting

Y. Benyahia; K. Yu; K. B. Smires; M. Jaggi; A. C. Davison et al. 

2019. ICML 2019 – 36th International Conference on Machine Learning, Long Beach, California, USA, June 09-15, 2019. p. 594-603.

Generating Artificial Data for Private Deep Learning

A. Triastcyn; B. Faltings 

2019. 

Geometry Aware Convolutional Filters for Omnidirectional Images Representation

R. Khasanova; P. Frossard 

2019. ICML 2019 – 36th International Conference on Machine Learning, Long Beach, CA, USA, Jan 18-23, 2019. p. 3351-3359.

A Representer Theorem for Deep Neural Networks

M. Unser 

Journal Of Machine Learning Research. 2019-01-01. Vol. 20.

Graph Laplacians for Rotation Equivariant Convolutional Neural Networks

M. Milani 

2019.

Graph-based image representation learning

R. Khasanova / P. Frossard (Dir.)  

Lausanne, EPFL, 2019. 

Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning

M. El Helou; S. Süsstrunk 

2019

Deep Learning for Music: Similarity Search and Beyond

M. G. M. A. Devaux 

2019

Deep Micro-Dictionary Learning and Coding Network

H. Tang; H. Wei; W. Xiao; W. Wang; D. Xu et al. 

2019-01-01. 19th IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, Jan 07-11, 2019. p. 386-395. DOI : 10.1109/WACV.2019.00047.

Rethinking Person Re-Identification with Confidence

G. Adaimi; S. Kreiss; A. Alahi 

2019. arXiv.

Learning How To Recognize Faces In Heterogeneous Environments

T. De Freitas Pereira / H. Bourlard; S. Marcel (Dir.)  

Lausanne, EPFL, 2019. 

Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

A. J. Mosinska / P. Fua (Dir.)  

Lausanne, EPFL, 2019. 

Joint Segmentation and Path Classification of Curvilinear Structures

A. J. Mosinska; M. Kozinski; P. Fua 

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). 2019. 

EMPIRICAL EVALUATION AND COMBINATION OF PUNCTUATION PREDICTION MODELS APPLIED TO BROADCAST NEWS

A. Nanchen; P. N. Garner 

2019

Deep Residual Output Layers for Neural Language Generation

N. Pappas; J. Henderson 

2019. Proceedings of the 36th International Conference on Machine Learning (ICML).

[Re] Meta learning with differentiable closed-form solvers

A. Devos; S. Chatel; M. Grossglauser 

The ReScience journal C. 2019. 

Reproducing Meta-learning with differentiable closed-form solvers

A. Devos; S. Chatel; M. Grossglauser 

2019. Reproducibility in Machine Learning Workshop (ICLR), New Orleans, Louisiana, USA, May 6-9, 2019.

Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing

O. Nikisins; A. George; S. Marcel 

2019. International Conference on Biometrics 2019, IEEE.

A Generalized Representer Theorem for Hilbert Space – Valued Functions

S. S. Diwale; C. Jones 

2019

Mobile Robotic Painting of Texture

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

2019. ICRA 2019 – IEEE International Conference on Robotics and Automation, Montreal, Canada, May 20-24, 2019.

Design of approximate and precision-scalable circuits for embedded multimedia and neural-network processing

V. F. Camus / C. Enz (Dir.)  

Lausanne, EPFL, 2019. 

A Deep Learning Approach for Robust Head Pose Independent Eye Movements Recognition from Videos

R. Siegfried; Y. Yu; J-M. Odobez 

2019. 2019 ACM Symposium on Eye Tracking Research & Applications. p. 5. DOI : 10.1145/3314111.3319844.

Sparse and Low-rank Modeling for Automatic Speech Recognition

P. Dighe / H. Bourlard (Dir.)  

Lausanne, EPFL, 2019. 

End-to-End Acoustic Modeling using Convolutional Neural Networks for HMM-based Automatic Speech Recognition

D. Palaz; M. Magimai.-Doss; R. Collobert 

Speech Communication. 2019. Vol. 108, p. 15-32. DOI : 10.1016/j.specom.2019.01.004.

EMPIRICAL EVALUATION AND COMBINATION OF PUNCTUATION PREDICTION MODELS APPLIED TO BROADCAST NEWS

A. Nanchen; P. N. Garner 

2019. Proceedings of 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Learning voice source related information for depression detection

S. P. Dubagunta; B. Vlasenko; M. Magimai.-Doss 

2019. 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, ENGLAND, May 12-17, 2019. p. 6525-6529.

Incorporating Deep Bagging Ensemble Method as a Surrogate Model for Simulating Hyper-Concentrated Sediment-Laden Flows

S. M. Hamze-Ziabari; A. Yeganeh-Bakhtiary; S. M. Siadatmousavi; D. A. Barry 

1st Swiss Workshop on Machine Learning for Environmental and Geosciences 2019, Zurich, Switzerland, 16-17 January 2019.

DeepSuccess – Predict the Success of Tech Startups

D. Di Dio 

2019

Joint Fusion Learning of Multiple Time Series Prediction

O. Gümüş 

2019

A comparative study on wavelets and residuals in deep super resolution

R. Zhou; F. Lahoud; M. El Helou; S. Süsstrunk 

2019. 2019 IS&T International Symposium on Electronic Imaging, Burlingame, California USA, 13 – 17 January, 2019.

Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

J. Vázquez-Canteli; S. Ulyanin; J. Kämpf; Z. Nagy 

Sustainable Cities and Society. 2019. Vol. 45, p. 243-257. DOI : 10.1016/j.scs.2018.11.021.

Heterogeneous Face Recognition Using Domain Specific Units

T. de Freitas Pereira; A. Anjos; S. Marcel 

IEEE Transactions on Information Forensics and Security. 2019.  p. 13. DOI : 10.1109/TIFS.2018.2885284.

Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning

C. Chen; Y. Liu; S. Kreiss; A. Alahi 

2019. International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 20-24, 2019.

2018

Let Me Not Lie: Learning MultiNomial Logit

B. Sifringer; V. Lurkin; A. Alahi 

arxiv. 2018-12-23. 

Autonomous illumination control for localization microscopy

M. Stefko; B. Ottino; K. M. Douglass; S. Manley 

Optics Express. 2018-11-12. Vol. 26, num. 23, p. 30882-30900. DOI : 10.1364/OE.26.030882.

Cross-lingual Adaptation of a CTC-based multilingual Acoustic Model

S. Tong; P. N. Garner; H. Bourlard 

Speech Communication. 2018-11-01. Vol. 104, p. 39-46. DOI : 10.1016/j.specom.2018.09.001.

Multimode optical fiber transmission with a deep learning network

B. Rahmani; D. Loterie; G. Konstantinou; D. Psaltis; C. Moser 

Light-Science & Applications. 2018-10-03. Vol. 7, p. 96. DOI : 10.1038/s41377-018-0074-1.

Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network

D. Mahapatra; B. Bozorgtabar; J-P. Thiran; M. Reyes 

2018-09-16. 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain, September 16-20. DOI : 10.1007/978-3-030-00934-2_65.

Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

F. La Rosa; M. J. Fartaria; T. Kober; J. Richiardi; C. Granziera et al. 

2018-09-10. BrainLes Workshop, MICCAI 2018, Granada, Spain, September 16, 2018. DOI : 10.1007/978-3-030-11723-8_14.

dhSegment : A generic deep-learning approach for document segmentation

S. Ares Oliveira; B. L. A. Seguin; F. Kaplan 

The 16th International Conference on Frontiers in Handwriting Recognition, Niagara Falls, USA, 5-8 August 2018.

PCA and deep learning based myoelectric grasping control of a prosthetic hand

C. Li; J. Ren; H. Huang; B. Wang; Y. Zhu et al. 

Biomedical Engineering Online. 2018-08-06. Vol. 17, p. 107. DOI : 10.1186/s12938-018-0539-8.

Comparing human and machine performances in transcribing 18th century handwritten Venetian script

S. Ares Oliveira; F. Kaplan 

2018-07-26. Digital Humanities Conference, Mexico City, Mexico, June 24-29, 2018.

Comparing human and machine performances in transcribing 18th century handwritten Venetian script

S. Ares Oliveira 

Digital Humanities Conference, Mexico City, Mexico, June 25-29.

Deep learning on graph for semantic segmentation of point cloud

A. Cherqui 

2018-07-02.