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.


Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

A. Gratsea; P. Huembeli 

Quantum Machine Intelligence. 2022-06-01. Vol. 4, num. 1, p. 2. DOI : 10.1007/s42484-021-00058-6.

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

A. Momeni; R. Fleury 

Nature Communications. 2022-05-12. Vol. 13, num. 2651, p. 1-11. DOI : 10.1038/s41467-022-30297-5.

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

V. Pandiyan; R. Drissi-Daoudi; S. Shevchik; G. Masinelli; Tri Le-Quang et al. 

Journal Of Materials Processing Technology. 2022-05-01. Vol. 303, p. 117531. DOI : 10.1016/j.jmatprotec.2022.117531.

An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach

A. Heidari; F. Marechal; D. Khovalyg 

Applied Energy. 2022-04-15. Vol. 312, p. 118833. DOI : 10.1016/j.apenergy.2022.118833.

ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning

J. A. H. Klein; i. Boybat; Y. M. Qureshi; M. Dazzi; A. S. J. Levisse et al. 

arXiv. 2022-04-07. 

Impact and implications of mixed plaque class in automated characterization of complex atherosclerotic lesions

M. L. Olender; Y. Niu; D. Marlevi; E. R. Edelman; F. R. Nezami 

Computerized Medical Imaging And Graphics. 2022-04-01. Vol. 97, p. 102051. DOI : 10.1016/j.compmedimag.2022.102051.

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

P. Danassis; Z. D. Erden; B. Faltings 

Autonomous Agents And Multi-Agent Systems. 2022-04-01. Vol. 36, num. 1, p. 13. DOI : 10.1007/s10458-021-09541-7.

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI

F. La Rosa; E. S. Beck; J. Maranzano; R-A. Todea; P. van Gelderen et al. 

Nmr In Biomedicine. 2022-03-31.  p. e4730. DOI : 10.1002/nbm.4730.

Wind-Topo: Downscaling near-surface wind fields to high-resolution topography in highly complex terrain with deep learning

J. Dujardin; M. Lehning 

Quarterly Journal Of The Royal Meteorological Society. 2022-03-29. DOI : 10.1002/qj.4265.

Learnable filter-banks for CNN-based audio applications

H. Peic Tukuljac; B. Ricaud; N. Aspert; L. Colbois 

2022-03-28. 5th Northern Lights Deep Learning Conference (NLDL 2022), Tromsø, Norway, January 10-12, 2022. DOI : 10.7557/18.6279.

A Deep-Learning Approach to Side-Channel Based CPU Disassembly at Design Time

H. Fendri; M. Macchetti; J. Perrine; M. Stojilovic 

2022-03-22. 25th Design, Automation and Test in Europe – DATE 2022, Antwerp, Belgium [Virtual], March 14-23, 2022.

CryoETGAN: Cryo-Electron Tomography Image Synthesis via Unpaired Image Translation

X. Wu; C. Li; X. Zeng; H. Wei; H-W. Deng et al. 

Frontiers In Physiology. 2022-03-04. Vol. 13, p. 760404. DOI : 10.3389/fphys.2022.760404.

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

R. Ignatans; M. Ziatdinov; R. Vasudevan; M. Valleti; V. Tileli et al. 

Advanced Functional Materials. 2022-03-04.  p. 2100271. DOI : 10.1002/adfm.202100271.

Statistical distortion of supervised learning predictions in optical microscopy induced by image compression

E. Pomarico; C. Schmidt; F. Chays; D. Nguyen; A. Planchette et al. 

Scientific Reports. 2022-03-02. Vol. 12, num. 1, p. 3464. DOI : 10.1038/s41598-022-07445-4.

A Practical Guide to Supervised Deep Learning for Bioimage Analysis: Challenges and good practices

V. Uhlmann; L. Donati; D. Sage 

Ieee Signal Processing Magazine. 2022-03-01. Vol. 39, num. 2, p. 73-86. DOI : 10.1109/MSP.2021.3123589.

Semi-supervised Active Salient Object Detection

Y. Lv; B. Liu; J. Zhang; Y. Dai; A. Li et al. 

Pattern Recognition. 2022-03-01. Vol. 123, p. 108364. DOI : 10.1016/j.patcog.2021.108364.

Impulsive noise removal via a blind CNN enhanced by an iterative post-processing

S. Sadrizadeh; H. Otroshi-Shahreza; F. Marvasti 

Signal Processing. 2022-03-01. Vol. 192, p. 108378. DOI : 10.1016/j.sigpro.2021.108378.

Editorial: Computational Neuroimage Analysis Tools for Brain (Diseases) Biomarkers

D. M. Sima; M. Bach Cuadra; T. B. Dyrby; K. Van Leemput 

Frontiers In Neuroscience. 2022-02-18. Vol. 16, p. 841807. DOI : 10.3389/fnins.2022.841807.

Magnetic control of tokamak plasmas through deep reinforcement learning

J. Degrave; F. Felici; J. Buchli; M. Neunert; B. Tracey et al. 

Nature. 2022-02-16. Vol. 602, num. 7897, p. 414-419. DOI : 10.1038/s41586-021-04301-9.

High-speed identification of suspended carbon nanotubes using Raman spectroscopy and deep learning

J. Zhang; M. L. Perrin; L. Barba; J. Overbeck; S. Jung et al. 

Microsystems & Nanoengineering. 2022-02-10. Vol. 8, num. 1, p. 19. DOI : 10.1038/s41378-022-00350-w.

Automatic table detection and classification in large-scale newspaper archives

A. Vernet 


Photo-astrometric distances, extinctions, and astrophysical parameters for Gaia EDR3 stars brighter than G=18.5

F. Anders; A. Khalatyan; A. B. A. Queiroz; C. Chiappini; J. Ardevol et al. 

Astronomy & Astrophysics. 2022-02-07. Vol. 658, p. A91. DOI : 10.1051/0004-6361/202142369.

Deep Learning Detection of GPS Spoofing

O. Jullian; B. Otero; M. Stojilović; J. J. Costa; J. Verdú et al. 

2022-02-02. 7th International Conference Machine Learning, Optimization, and Data Science (LOD 2021), Grasmere, UK, October 4-8, 2021. p. 527-540. DOI : 10.1007/978-3-030-95467-3_38.

Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques

L. Yao; B. Zhu; M. Shoaran 

Journal Of Neural Engineering. 2022-02-01. Vol. 19, num. 1, p. 016037. DOI : 10.1088/1741-2552/ac4ed1.

Align, then memorise: the dynamics of learning with feedback alignment*

M. Refinetti; S. D’Ascoli; R. Ohana; S. Goldt 

Journal Of Physics A-Mathematical And Theoretical. 2022-01-28. Vol. 55, num. 4, p. 044002. DOI : 10.1088/1751-8121/ac411b.

Is Your Time Well Spent Online?: Focusing on Quality Experiences Through a User-Centered Recommendation Algorithm and Simulation Model

R. Islambouli; S. Ingram; D. Gillet 

2022-01-25. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), [Virtual event] Pasadena, CA, USA , December 13-16, 2021. DOI : 10.1109/ICMLA52953.2021.00171.

New particle formation event detection with Mask R-CNN

P. Su; J. Joutsensaari; L. Dada; M. A. Zaidan; T. Nieminen et al. 

Atmospheric Chemistry and Physics. 2022-01-25. Vol. 22, num. 2, p. 1293-1309. DOI : 10.5194/acp-22-1293-2022.

Learning to image and compute with multimode optical fibers

B. Rahmani; I. Oguz; U. Tegin; J-l. Hsieh; D. Psaltis et al. 

Nanophotonics. 2022-01-21. DOI : 10.1515/nanoph-2021-0601.

Euclid preparation XIII. Forecasts for galaxy morphology with the Euclid Survey using deep generative models

H. Bretonniere; M. Huertas-Company; A. Boucaud; F. Lanusse; E. Jullo et al. 

Astronomy & Astrophysics. 2022-01-18. Vol. 657, p. A90. DOI : 10.1051/0004-6361/202141393.

Deep Learning and Earth Observation to Support the Sustainable Development Goals: Current Approaches, Open Challenges, and Future Opportunities

C. Persello; J. D. Wegner; R. Haensch; D. Tuia; P. Ghamisi et al. 

Ieee Geoscience And Remote Sensing Magazine. 2022-01-13. DOI : 10.1109/MGRS.2021.3136100.

BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration

M. El Helou; S. Süsstrunk 

IEEE Transactions on Image Processing. 2022-01-08. Vol. 31, p. 1-13. DOI : 10.1109/TIP.2022.3143006.

Learning to sample in Cartesian MRI

T. Sanchez / V. Cevher (Dir.)  

Lausanne, EPFL, 2022. 

Optimization Over Banach Spaces: A Unified View on Supervised Learning and Inverse Problems

S. Aziznejad / M. Unser (Dir.)  

Lausanne, EPFL, 2022. 

Graph Representation Learning with Optimal Transport: Analysis and Applications

E. Simou / P. Frossard (Dir.)  

Lausanne, EPFL, 2022. 

Using Animal Motion Capture to Learn Neural Representations

S. Günel / P. Fua; P. P. Ramdya (Dir.)  

Lausanne, EPFL, 2022. 

Combining Model Driven and Data Driven Approaches for Inverse Problems in Parameter Estimation and Image Reconstruction: From Modelling to Validation

T. Yu / J-P. Thiran; M. Bach Cuadra (Dir.)  

Lausanne, EPFL, 2022. 

Deep learning accelerated prediction of the permeability of fibrous microstructures

B. Caglar; G. Broggi; M. A. Ali; L. Orgéas; V. Michaud 

Composites Part A: Applied Science and Manufacturing. 2022. Vol. 158, p. 106973. DOI : 10.1016/j.compositesa.2022.106973.

Synthesis and Analysis of 3D shapes with Geometric Deep Learning in Computer-Aided Engineering

E. Remelli / P. Fua (Dir.)  

Lausanne, EPFL, 2022. 

Sparsest Univariate Learning Models Under Lipschitz Constraint

S. Aziznejad; T. Debarre; M. Unser 

Ieee Open Journal Of Signal Processing. 2022-01-01. Vol. 3, p. 140-154. DOI : 10.1109/OJSP.2022.3157082.

Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions

Z. Harteveld / B. E. Ferreira De Sousa Correia (Dir.)  

Lausanne, EPFL, 2022. 

Targeting molecular surfaces to engineer novel protein-based immunogens and inhibitors

S. Wehrle / B. E. Ferreira De Sousa Correia (Dir.)  

Lausanne, EPFL, 2022. 

Essays in Empirical Asset Pricing

A. A. Marchal / P. Collin Dufresne; J. Hugonnier (Dir.)  

Lausanne, EPFL, 2022. 

Reshaping Perception for Autonomous Driving with Semantic Keypoints

L. Bertoni / A. M. Alahi (Dir.)  

Lausanne, EPFL, 2022. 

Learning of physical systems: from inference to control

B. Rahmani / C. Moser (Dir.)  

Lausanne, EPFL, 2022. 

Learning to Align Sequential Actions in the Wild

W. Liu; B. Tekin; H. Coskun; V. Vineet; P. Fua et al. 

2022. CVPR 2022 : IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans , United States, June 21 – 24, 2022.

TEE-based decentralized recommender systems: The raw data sharing redemption

A. Dhasade; N. Dresevic; A-M. Kermarrec; R. Pires 

2022. 36th IEEE International Parallel & Distributed Processing Symposium (IPDPS ’22), Virtual, May 30 – June 3 2022. DOI : 10.1109/IPDPS53621.2022.00050.

Deep learning-based analysis of multiple sclerosis lesions with high and ultra-high field MRI

F. La Rosa / J-P. Thiran; M. Bach Cuadra (Dir.)  

Lausanne, EPFL, 2022. 

Vision-based Flocking in Aerial Robot Swarms

F. M. Schilling / D. Floreano (Dir.)  

Lausanne, EPFL, 2022. 

Textual Explanations and Critiques in Recommendation Systems

D. M. Antognini / B. Faltings (Dir.)  

Lausanne, EPFL, 2022. 

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

D. Guo; T. Mordan; A. Alahi 

2022. IEEE 39th International Conference on Robotics and Automation (ICRA 2022), Philadelphia, Pennsylvania, USA, May 23-27, 2022.

Encoder-Decoder Models for Human Segmentation and Motion Analysis

I. Katircioglu / P. Fua; M. Salzmann (Dir.)  

Lausanne, EPFL, 2022. 

Learning to Represent and Reconstruct 3D Deformable Objects

J. Bednarík / P. Fua; M. Salzmann (Dir.)  

Lausanne, EPFL, 2022. 

Graph Representation Learning in Computational Pathology

G. Jaume / J-P. Thiran; M. Gabrani (Dir.)  

Lausanne, EPFL, 2022. 

Controllability and Interpretability in Affective Speech Synthesis

B. Schnell / H. Bourlard; P. N. Garner (Dir.)  

Lausanne, EPFL, 2022. 

Scalable Multi-agent Coordination and Resource Sharing

P. Danassis / B. Faltings (Dir.)  

Lausanne, EPFL, 2022. 

Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization

T. R. Lane; F. Urbina; L. Rank; J. Gerlach; O. Riabova et al. 

Molecular Pharmaceutics. 2022. Vol. 19, num. 2, p. 674–689. DOI : 10.1021/acs.molpharmaceut.1c00791.

Infrared nanoplasmonic metasurfaces augmented by artificial intelligence for universal biosensing

A. M. John-Herpin / H. Altug (Dir.)  

Lausanne, EPFL, 2022. 

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. 2022. Vol. 44, num. 1, p. 181-195. DOI : 10.1109/TPAMI.2020.3010886.


Data-driven myelin water imaging based on T-1 and T-2 relaxometry

G. F. Piredda; T. Hilbert; V. Ravano; E. J. Canales-Rodriguez; M. Pizzolato et al. 

Nmr In Biomedicine. 2021-12-22.  p. e4668. DOI : 10.1002/nbm.4668.

FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows

E. Simou 


Generative Adversarial Networks for Localized Vibrotactile Feedback in Haptic Surfaces

C. Hernandez Mejia; X. Ren; A. Thabuis; J. Chavanne; P. Germano et al. 

2021-12-17. 2021 24th International Conference on Electrical Machines and Systems (ICEMS), Gyeongju, Republic of Korea (hybrid), October 31 – November 3, 2021. p. 105-110. DOI : 10.23919/ICEMS52562.2021.9634513.

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

B. A. Illing; J. Ventura; G. Bellec; W. Gerstner 

35th Conference on Neural Information Processing Systems (NeurIPS 2021), Online, December 6-14, 2021.

What can linearized neural networks actually say about generalization?

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

2021-12-06. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, December 6-14, 2021.

deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling

A. G. Mullissa; D. Marcos; D. Tuia; M. Herold; J. Reiche 

IEEE Transactions on Geoscience and Remote Sensing. 2021-12-06. Vol. 60, num. 5200315, p. 1-15. DOI : 10.1109/TGRS.2020.3042694.

Neural interface systems with on-device computing: machine learning and neuromorphic architectures

J. Yoo; M. Shoaran 

Current Opinion In Biotechnology. 2021-12-01. Vol. 72, p. 95-101. DOI : 10.1016/j.copbio.2021.10.012.

Generalisation error in learning with random features and the hidden manifold model*

F. Gerace; B. Loureiro; F. Krzakala; M. Mezard; L. Zdeborova 

Journal Of Statistical Mechanics-Theory And Experiment. 2021-12-01. Vol. 2021, num. 12, p. 124013. DOI : 10.1088/1742-5468/ac3ae6.

Time-Dependent Deep Image Prior for Dynamic MRI

J. Yoo; K. H. Jin; H. Gupta; J. Yerly; M. Stuber et al. 

Ieee Transactions On Medical Imaging. 2021-12-01. Vol. 40, num. 12, p. 3337-3348. DOI : 10.1109/TMI.2021.3084288.

Counting using deep learning regression gives value to ecological surveys

J. P. A. Hoekendijk; B. Kellenberger; G. Aarts; S. Brasseur; S. S. H. Poiesz et al. 

Scientific Reports. 2021-12-01. Vol. 11, num. 1, p. 1-12, 23209. DOI : 10.1038/s41598-021-02387-9.

Spectral Measurement and Classification in the Era of Big Data

F. S. Webler; M. Andersen 

2021-11-22. CIE 2021, Malaysia online, September 27-29, 2021. DOI : 10.25039/x48.2021.OP15.

Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies

L. Di Natale; B. Svetozarevic; P. Heer; C. Jones 

2021-11-19. CISBAT 2021 – Carbon Neutral Cities: Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, September 8-10, 2021. p. 012004. DOI : 10.1088/1742-6596/2042/1/012004.

Reinforcement Learning and Hardware in the Loop for Localized Vibrotactile Feedback in Haptic Surfaces

C. Hernandez Mejia; M. Favier; X. Ren; P. Germano; Y. Perriard 

2021-11-15. IEEE International Ultrasonics Symposium (IUS) 2021, Xi’an, China (virtual symposium), September 11-16, 2021. DOI : 10.1109/IUS52206.2021.9593749.

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

V. Bikia; M. Lazaroska; D. Scherrer Ma; M. Zhao; G. Rovas et al. 

Frontiers In Bioengineering And Biotechnology. 2021-10-27. Vol. 9, p. 754003. DOI : 10.3389/fbioe.2021.754003.

Population pharmacokinetic model selection assisted by machine learning

E. Sibieude; A. Khandelwal; P. Girard; J. S. Hesthaven; N. Terranova 

Journal Of Pharmacokinetics And Pharmacodynamics. 2021-10-27. DOI : 10.1007/s10928-021-09793-6.

Deep Neural Network to Accurately Predict Left Ventricular Systolic Function Under Mechanical Assistance

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

Frontiers In Cardiovascular Medicine. 2021-10-26. Vol. 8, p. 752088. DOI : 10.3389/fcvm.2021.752088.

Social media and deep learning capture the aesthetic quality of the landscape

I. Havinga; D. Marcos; P. W. Bogaart; L. Hein; D. Tuia 

Scientific Reports. 2021-10-08. Vol. 11, p. 1-11, 20000. DOI : 10.1038/s41598-021-99282-0.

Benchmarking of objective quality metrics for point cloud compression

D. Lazzarotto; E. Alexiou; T. Ebrahimi 

2021-10-06. IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP 2021), Tampere, Finland, October 06-08, 2021.

A User Centered News Recommendation System

R. Islambouli; S. Ingram; D. Gillet 

2021-10-05. 32nd ACM Conference on Hypertext and Social Media (HT ’21), Virtual Event Ireland, August 30–September 3, 2021. p. 15-16. DOI : 10.1145/3468143.3483931.

Toward fully automated assessment of the central vein sign using deep learning

T. Huelnhagen; O. Al Louzi; M. J. Fartaria; L. Daboul; D. S. Reich et al. 

2021-10-01.  p. 461-462.

Hand-Object Interaction: From Human Demonstrations to Robot Manipulation

A. Carfi; T. Patten; Y. Kuang; A. Hammoud; M. Alameh et al. 

Frontiers In Robotics And Ai. 2021-10-01. Vol. 8, p. 714023. DOI : 10.3389/frobt.2021.714023.

PM2.5 Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

K. Gu; H. Liu; Z. Xia; J. Qiao; W. Lin et al. 

Ieee Transactions On Neural Networks And Learning Systems. 2021-10-01. Vol. 32, num. 10, p. 4278-4290. DOI : 10.1109/TNNLS.2021.3105394.

Towards versatile conversations with data-driven dialog management and its integration in commercial platforms

P. Canas; D. Griol; Z. Callejas 

Journal Of Computational Science. 2021-10-01. Vol. 55, p. 101443. DOI : 10.1016/j.jocs.2021.101443.

DeepImageJ: A user-friendly environment to run deep learning models in ImageJ

E. Gomez-de-Mariscal; C. Garcia-Lopez-de-Haro; W. Ouyang; L. Donati; E. Lundberg et al. 

Nature Methods. 2021-09-30. DOI : 10.1038/s41592-021-01262-9.

Defect segmentation for multi-illumination quality control systems

D. Honzátko; E. Türetken; S. Arjomand Bigdeli; L. A. Dunbar; P. Fua 

Machine Vision and Applications. 2021-09-23. Vol. 32, num. 118, p. 16. DOI : 10.1007/s00138-021-01244-z.

POD-Enhanced Deep Learning-Based Reduced Order Models for the Real-Time Simulation of Cardiac Electrophysiology in the Left Atrium

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

Frontiers In Physiology. 2021-09-22. Vol. 12, p. 679076. DOI : 10.3389/fphys.2021.679076.

Deep eyes: Joint depth inference using monocular and binocular cues

Z. Chen; X. Guo; S. Li; Y. Yang; J. Yu 

Neurocomputing. 2021-09-17. Vol. 453, p. 812-824. DOI : 10.1016/j.neucom.2020.06.132.

Disparity Between Batches as a Signal for Early Stopping

M. Forouzesh; P. Thiran 

2021-09-13. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021), Bilbao, Basque Country, Spain, September 13-17, 2021. DOI : 10.1007/978-3-030-86520-7_14.

Deep learning architectures for estimating breathing signal and respiratory parameters from speech recordings

V. S. Nallanthighal; Z. Mostaani; A. Harma; H. Strik; M. Magimai-Doss 

Neural Networks. 2021-09-01. Vol. 141, p. 211-224. DOI : 10.1016/j.neunet.2021.03.029.

Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems

Z. Dang; K. M. Yi; Y. Hu; F. Wang; P. Fua et al. 

Ieee Transactions On Pattern Analysis And Machine Intelligence. 2021-09-01. Vol. 43, num. 9, p. 3167-3182. DOI : 10.1109/TPAMI.2020.2978812.

Learning from survey propagation: a neural network for MAX-E-3-SAT

R. Marino 

Machine Learning-Science And Technology. 2021-09-01. Vol. 2, num. 3, p. 035032. DOI : 10.1088/2632-2153/ac0496.

Physics-Inspired Structural Representations for Molecules and Materials

F. Musil; A. Grisafi; A. P. Bartok; C. Ortner; G. Csanyi et al. 

Chemical Reviews. 2021-08-25. Vol. 121, num. 16, p. 9759-9815. DOI : 10.1021/acs.chemrev.1c00021.

Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems : potential, issues and challenges

S. C. Koumetio Tekouabou; E. B. Diop; R. Azmi; R. Jaligot; J. Chenal 

Journal of King Saud University – Computer and Information Sciences. 2021-08-20. DOI : 10.1016/j.jksuci.2021.08.007.

The role of convolutional neural networks in scanning probe microscopy: a review

I. Azuri; I. Rosenhek-Goldian; N. Regev-Rudzki; G. Fantner; S. R. Cohen 

Beilstein Journal Of Nanotechnology. 2021-08-13. Vol. 12, p. 878-901. DOI : 10.3762/bjnano.12.66.

Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation

M. Dorokhova; Y. Martinson; C. Ballif; N. Wyrsch 

Applied Energy. 2021-08-05. Vol. 301, p. 117504. DOI : 10.1016/j.apenergy.2021.117504.

Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

X. Lin; D. Bhattacharjee; M. El Helou; S. Süsstrunk 

IEEE Signal Processing Letters. 2021-08-03. Vol. 28, num. SPL-30904-2021.R1, p. 1719-1723. DOI : 10.1109/LSP.2021.3104769.

Counting People by Estimating People Flows

W. Liu; M. Salzmann; P. Fua 

IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021-08-03. DOI : 10.1109/TPAMI.2021.3102690.

Context-Aware Learning for Generative Models

S. Perdikis; R. Leeb; R. Chavarriaga; J. d. R. Millan 

Ieee Transactions On Neural Networks And Learning Systems. 2021-08-01. Vol. 32, num. 8, p. 3471-3483. DOI : 10.1109/TNNLS.2020.3011671.

LiftPose3D, a deep learning-based approach for transforming two-dimensional to three-dimensional poses in laboratory animals

A. Gosztolai; S. Gunel; V. Lobato-Rios; M. Pietro Abrate; D. Morales et al. 

Nature Methods. 2021-08-01. Vol. 18, num. 8, p. 975-+. DOI : 10.1038/s41592-021-01226-z.

Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective

J. Zhang; Y. Dai; T. Zhang; M. Harandi; N. Barnes et al. 

Ieee Transactions On Pattern Analysis And Machine Intelligence. 2021-08-01. Vol. 43, num. 8, p. 2866-2873. DOI : 10.1109/TPAMI.2020.3046486.

Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

J. Krepl; F. Casalegno; E. Delattre; C. Ero; H. Lu et al. 

Frontiers In Neuroinformatics. 2021-07-28. Vol. 15, p. 691918. DOI : 10.3389/fninf.2021.691918.

A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19

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