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.


On the number of regions of piecewise linear neural networks

A. Goujon; A. Etemadi; M. Unser 

Journal of Computational and Applied Mathematics. 2024. Vol. 441, p. 115667. DOI : 10.1016/


Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis

J. M. Ha; O. Fink 

Mechanical Systems And Signal Processing. 2023-11-01. Vol. 202, p. 110680. DOI : 10.1016/j.ymssp.2023.110680.

Stable parameterization of continuous and piecewise-linear functions

A. Goujon; J. Campos; M. Unser 

Applied And Computational Harmonic Analysis. 2023-11-01. Vol. 67, p. 101581. DOI : 10.1016/j.acha.2023.101581.

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

S. Baghersalimi; A. Amirshahi; T. Teijeiro; A. Aminifar; D. Atienza Alonso 

2023-10-09. IEEE-EMBS International Conference on Body Sensor Networks: Sensors and Systems for Digital Health (IEEE BSN) 2023, Cambridge, MA, US, October 9-11, 2023.

Robust Estimation of the Microstructure of the Early Developing Brain Using Deep Learning

H. Kebiri; A. Gholipour; R. Lin; L. Vasung; D. Karimi et al. 

2023-10-01. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Vancouver, Canada, October 8-12, 2023. p. 293-303. DOI : 10.1007/978-3-031-43990-2_28.

Toward contactless human thermal monitoring: A framework for Machine Learning-based human thermo-physiology modeling augmented with computer vision

M. Rida; Mohamed Abdelfattah; Alexandre Alahi; Dolaana Khovalyg 

Building and Environment. 2023-09-26. Vol. 245, num. 110850. DOI : 10.1016/j.buildenv.2023.110850.

Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home

M. Motta; E. C. Groves; A. R. Schneider; S. Paoletti; N. Henchoz et al. 

PLOS Digital Health. 2023-09-15. DOI : 10.1371/journal.pdig.0000319.

Ageing-aware battery discharge prediction with deep learning

L. Biggio; T. Bendinelli; C. Kulkarni; O. Fink 

Applied Energy. 2023-09-15. Vol. 346, p. 121229. DOI : 10.1016/j.apenergy.2023.121229.

Emotional labor experienced in team-projects: A comparison of engineering and hospitality students

N. Kotluk; R. Tormey; R. Germanier; A. Darioly 

2023-09-11. 2023 SEFI Annual Conference, Dublin, Ireland, September 11-14, 2023, 2023-09-11.

Learning-based techniques for lensless reconstruction

Y. Perron 


Neural-prior stochastic block model

O. Duranthon; L. Zdeborová 

Machine Learning-Science And Technology. 2023-09-01. Vol. 4, num. 3, p. 035017. DOI : 10.1088/2632-2153/ace60f.

Towards learning-based image compression for storage on DNA support

S. Strebel; N. Monnier; D. Lazzarotto; M. Testolina; T. Ebrahimi 

2023-08-21. Applications of Digital Image Processing XLVI, San Diego, USA, August 21-23, 2023. p. 126740V. DOI : 10.1117/12.2677356.

Where Did the News Come From? Detection of News Agency Releases in Historical Newspapers

L. Marxen 


Ab initio quantum chemistry with neural-network wavefunctions

J. Hermann; J. Spencer; K. Choo; A. Mezzacapo; W. M. C. Foulkes et al. 

Nature Reviews Chemistry. 2023-08-09. DOI : 10.1038/s41570-023-00516-8.

DeePMD-kit v2: A software package for deep potential models

J. Zeng; D. Zhang; D. Lu; P. Mo; Z. Li et al. 

Journal Of Chemical Physics. 2023-08-07. Vol. 159, num. 5, p. 054801. DOI : 10.1063/5.0155600.

Identifying important sensory feedback for learning locomotion skills

W. Yu; C. Yang; C. McGreavy; E. Triantafyllidis; G. Bellegarda et al. 

Nature Machine Intelligence. 2023-08-01. Vol. 5, num. 8, p. 919-+. DOI : 10.1038/s42256-023-00701-w.

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

S. Romeni; E. Losanno; E. Koert; L. Pierantoni; I. Delgado-Martinez et al. 

Journal Of Neural Engineering. 2023-08-01. Vol. 20, num. 4, p. 046001. DOI : 10.1088/1741-2552/ace219.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

Y. Zhu; L-L. Chen; Y-W. Luo; L. Zhang; H-Y. Ma et al. 

European Radiology. 2023-07-12.  p. s00330-023-09845-0. DOI : 10.1007/s00330-023-09845-0.

Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses

L. Biggio; G. Vernardos; A. Galan; A. Peel; F. Courbin 

Astronomy & Astrophysics. 2023-07-11. Vol. 675, p. A125. DOI : 10.1051/0004-6361/202245126.

Self-correcting quantum many-body control using reinforcement learning with tensor networks

F. Metz; M. Bukov 

Nature Machine Intelligence. 2023-07-01. Vol. 5, num. 7, p. 780-791. DOI : 10.1038/s42256-023-00687-5.

Polynomial-time universality and limitations of deep learning

E. Abbe; C. Sandon 

Communications On Pure And Applied Mathematics. 2023-06-30. DOI : 10.1002/cpa.22121.

Reconstructing lensless image with ML models and deploying them onto embedded systems

J. P. Reymond 


Latent Representation of Computational Fluid Dynamics Meshes and Application to Airfoil Aerodynamics

Z. Wei; B. Guillard; P. Fua; V. Chapin; M. Bauerheim 

Aiaa Journal. 2023-06-23. DOI : 10.2514/1.J062533.

Towards learning-based denoising of light fields

T. Soares De Carvalho Feith; M. Testolina; T. Ebrahimi 

2023-06-07. Real-time Processing of Image, Depth and Video Information 2023, Prague, Czech Republic, 24-28 April 2023. DOI : 10.1117/12.2666000.

DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries

J. Heitmann; A. Glangetas; J. Doenz; J. N. Dervaux; D. Shama et al. 

Npj Digital Medicine. 2023-06-02. Vol. 6, num. 1, p. 104. DOI : 10.1038/s41746-023-00838-3.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

J. N. Siebert; M-A. Hartley; D. S. Courvoisier; M. Salamin; L. Robotham et al. 

Bmc Pulmonary Medicine. 2023-06-02. Vol. 23, num. 1, p. 191. DOI : 10.1186/s12890-022-02255-w.

Damage-augmented digital twins towards the automated inspection of buildings

B. G. Pantoja-Rosero; R. Achanta; K. Beyer 

Automation In Construction. 2023-06-01. Vol. 150, p. 104842. DOI : 10.1016/j.autcon.2023.104842.

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Y. Jiang; C. Liu; Z. Huang; M. Salzmann; S. Süsstrunk 

2023-06-01. 40th International Conference on Machine Learning (ICML 2023), Honolulu, Hawaii, USA, July 23-29, 2023.

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

K. J. Sandbrink; P. Mamidanna; C. Michaelis; M. Bethge; M. W. Mathis et al. 

Elife. 2023-05-31. Vol. 12, p. e81499. DOI : 10.7554/eLife.81499.

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

D. Milardovich; V. H. Souza; I. Zubarev; S. Tugin; J. O. Nieminen et al. 

Scientific Reports. 2023-05-22. Vol. 13, num. 1, p. 8225. DOI : 10.1038/s41598-023-34801-9.

Metal3D: a general deep learning framework for accurate metal ion location prediction in proteins

S. L. Durr; A. Levy; U. Rothlisberger 

Nature Communications. 2023-05-11. Vol. 14, num. 1, p. 2713. DOI : 10.1038/s41467-023-37870-6.

Leveraging Unlabeled Data to Track Memorization

M. Forouzesh; H. Sedghi; P. Thiran 

2023-05-01. 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda, May 1-5, 2023.

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

S. Kim; S. Kim; D. Min; P. Frossard; K. Sohn 

Ieee Transactions On Pattern Analysis And Machine Intelligence. 2023-05-01. Vol. 45, num. 5, p. 6372-6385. DOI : 10.1109/TPAMI.2022.3207286.

ReLU Neural Network Galerkin BEM

R. Aylwin; F. Henriquez; C. Schwab 

Journal Of Scientific Computing. 2023-05-01. Vol. 95, num. 2, p. 41. DOI : 10.1007/s10915-023-02120-w.

De novo design of protein interactions with learned surface fingerprints

P. Gainza Cirauqui; S. Wehrle; A. K. Van Hall-Beauvais; A. Marchand; A. Scheck et al. 

Nature. 2023-04-26. Vol. 617, num. 7959, p. 176-184. DOI : 10.1038/s41586-023-05993-x.

Two-dimensional Pure Isotropic Proton Solid State NMR

P. Moutzouri; M. Cordova; B. S. de Almeida; D. Torodii; L. Emsley 

Angewandte Chemie-International Edition. 2023-04-18. DOI : 10.1002/anie.202301963.

Three-dimensional nanoimaging of fuel cell catalyst layers

R. Girod; T. Lazaridis; H. A. Gasteiger; V. Tileli 

Nature Catalysis. 2023-04-17. DOI : 10.1038/s41929-023-00947-y.

Multi-agent reinforcement learning with graph convolutional neural networks for optimal bidding strategies of generation units in electricity markets

P. Rokhforoz; M. Montazeri; O. Fink 

Expert Systems With Applications. 2023-04-13. Vol. 225, p. 120010. DOI : 10.1016/j.eswa.2023.120010.

A generalized reinforcement learning based deep neural network agent model for diverse cognitive constructs

S. S. Nair; V. R. Muddapu; C. Vigneswaran; P. P. Balasubramani; D. S. Ramanathan et al. 

Scientific Reports. 2023-04-12. Vol. 13, num. 1, p. 5928. DOI : 10.1038/s41598-023-32234-y.

RMAML: Riemannian meta-learning with orthogonality constraints

H. Tabealhojeh; P. Adibi; H. Karshenas; S. K. Roy; M. Harandi 

Pattern Recognition. 2023-04-12. Vol. 140, p. 109563. DOI : 10.1016/j.patcog.2023.109563.

Convolutional neural network classifies visual stimuli from cortical response recorded with wide-field imaging in mice

D. De Luca; S. Moccia; L. Lupori; R. Mazziotti; T. Pizzorusso et al. 

Journal Of Neural Engineering. 2023-04-01. Vol. 20, num. 2, p. 026031. DOI : 10.1088/1741-2552/acc2e7.

Perspective Aware Road Obstacle Detection

K. Lis; S. Honari; P. Fua; M. Salzmann 

Ieee Robotics And Automation Letters. 2023-04-01. Vol. 8, num. 4, p. 2150-2157. DOI : 10.1109/LRA.2023.3245410.

A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission

A. Tykhonov; A. Kotenko; P. Coppin; M. Deliyergiyev; D. Droz et al. 

Astroparticle Physics. 2023-04-01. Vol. 146, p. 102795. DOI : 10.1016/j.astropartphys.2022.102795.

Visual question answering from another perspective: CLEVR mental rotation tests *

C. Beckham; M. Weiss; F. Golemo; S. Honari; D. Nowrouzezahrai et al. 

Pattern Recognition. 2023-04-01. Vol. 136, p. 109209. DOI : 10.1016/j.patcog.2022.109209.

CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets

L. Burgy; M. Weigert; G. Hatzopoulos; M. Minder; A. Journe et al. 

Bmc Bioinformatics. 2023-03-28. Vol. 24, num. 1, p. 120. DOI : 10.1186/s12859-023-05214-2.

Filter-Informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis

T. Li; C. Sun; O. Fink; Y. Yang; X. Chen et al. 

Ieee Transactions On Cybernetics. 2023-03-23. DOI : 10.1109/TCYB.2023.3256080.

The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning star

L. Cabayol; M. Eriksen; J. Carretero; R. Casas; F. J. Castander et al. 

Astronomy & Astrophysics. 2023-03-21. Vol. 671, p. A153. DOI : 10.1051/0004-6361/202245027.

Euclid preparation XXII. Selection of quiescent galaxies from mock photometry using machine learning

A. Humphrey; L. Bisigello; P. A. C. Cunha; M. Bolzonella; S. Fotopoulou et al. 

Astronomy & Astrophysics. 2023-03-14. Vol. 671, p. A99. DOI : 10.1051/0004-6361/202244307.

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

V. Swamy; S. Du; M. Marras; T. Käser 

2023-03-13. LAK 2023: The 13th International Learning Analytics and Knowledge Conference, Arlington, Texas, USA, March 13-17, 2023. DOI : 10.1145/3576050.3576147.

Aiming beyond slight increases in accuracy

D. Probst 

Nature Reviews Chemistry. 2023-03-09. DOI : 10.1038/s41570-023-00480-3.

Dynamics of social media behavior before and after SARS-CoV-2 infection

F. Durazzi; F. Pichard; D. Remondini; M. Salathe 

Frontiers In Public Health. 2023-02-23. Vol. 10, p. 1069931. DOI : 10.3389/fpubh.2022.1069931.

Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach

A-C. Billault-Roux; G. Ghiggi; L. Jaffeux; A. Martini; N. Viltard et al. 

Atmospheric Measurement Techniques. 2023-02-21. Vol. 16, num. 4, p. 911-940. DOI : 10.5194/amt-16-911-2023.

Social media and deep learning reveal specific cultural preferences for biodiversity

I. Havinga; D. Marcos; P. Bogaart; D. Massimino; L. Hein et al. 

People and Nature. 2023-02-19. Vol. 5, num. 3, p. 981-998. DOI : 10.1002/pan3.10466.

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

G. Michau; G. M. Frusque; O. Fink 

Proceedings of the National Academy of Sciences. 2023-02-18. Vol. 119, num. 8, p. e2106598119. DOI : 10.1073/pnas.2106598119.

Computer-Assisted Diagnosis of Lymph Node Metastases in Colorectal Cancers Using Transfer Learning With an Ensemble Model

A. Khan; N. Brouwer; A. Blank; F. Mueller; D. Soldini et al. 

Modern Pathology. 2023-02-17. Vol. 36, num. 5, p. 100118. DOI : 10.1016/j.modpat.2023.100118.

Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images

L. Bisigello; C. J. Conselice; M. Baes; M. Bolzonella; M. Brescia et al. 

Monthly Notices Of The Royal Astronomical Society. 2023-02-15. Vol. 520, num. 3, p. 3529-3548. DOI : 10.1093/mnras/stac3810.

DeepBND: A machine learning approach to enhance multiscale solid mechanics

F. Rocha; S. Deparis; P. Antolin; A. Buffa 

Journal of Computational Physics. 2023-02-14. Vol. 479, p. 111996. DOI : 10.1016/

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

S. Israilov; L. Fu; J. Sanchez-Rodriguez; F. Fusco; G. Allibert et al. 

Plos One. 2023-02-13. Vol. 18, num. 2, p. e0280071. DOI : 10.1371/journal.pone.0280071.

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

A. Rodriguez; C. Lin; H. Yang; M. Al-Fahdi; C. Shen et al. 

Npj Computational Materials. 2023-02-06. Vol. 9, num. 1, p. 20. DOI : 10.1038/s41524-023-00974-0.

Generative power of a protein language model trained on multiple sequence alignments

D. Sgarbossa; U. Lupo; A-F. Bitbol 

Elife. 2023-02-03. Vol. 12, p. e79854. DOI : 10.7554/eLife.79854.

Geodesic Convolutional Neural Network Characterization of Macro-Porous Latent Thermal Energy Storage

N. Mallya; P. B. Baqué; P. Yvernay; A. Pozzetti; P. Fua et al. 

ASME Journal of Heat and Mass Transfer. 2023-02-03. Vol. 145, num. 5, p. 052902. DOI : 10.1115/1.4056663.

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

E. Sibilano; A. Brunetti; D. Buongiorno; M. Lassi; A. Grippo et al. 

Journal Of Neural Engineering. 2023-02-01. Vol. 20, num. 1, p. 016048. DOI : 10.1088/1741-2552/acb96e.

End-to-end learned early classification of time series for in-season crop type mapping

M. Rußwurm; N. Courty; R. Emonet; S. Lefèvre; D. Tuia et al. 

ISPRS Journal of Photogrammetry and Remote Sensing. 2023-01-25. Vol. 196, p. 445-456. DOI : 10.1016/j.isprsjprs.2022.12.016.

Predicting the liveability of Dutch cities with aerial images and semantic intermediate concepts

A. Levering; D. Marcos; J. van Vliet; D. Tuia 

Remote Sensing of Environment. 2023-01-25. Vol. 287, p. 113454. DOI : 10.1016/j.rse.2023.113454.

Fusing physics-based and deep learning models for prognostics

M. Arias Chao; C. kulkarni; K. Goebel; O. Fink 

Reliability Engineering & System Safety. 2023-01-22. Vol. 217, p. 107961. DOI : 10.1016/j.ress.2021.107961.

Informing Neural Networks with Simplified Physics for Better Flow Prediction

F. Sergeev 


Behavioral outcome of very preterm children at 5 years of age: Prognostic utility of brain tissue volumes at term-equivalent-age, perinatal, and environmental factors

M. C. Liverani; S. Loukas; L. Gui; M-P. Pittet; M. Pereira et al. 

Brain And Behavior. 2023-01-14. DOI : 10.1002/brb3.2818.

Pure Isotropic Proton NMR Spectra in Solids using Deep Learning

M. Cordova; P. Moutzouri; B. S. de Almeida; D. Torodii; L. Emsley 

Angewandte Chemie-International Edition. 2023-01-13. Vol. 62, num. 8, p. e202216607. DOI : 10.1002/anie.202216607.

Facilitated machine learning for image-based fruit quality assessment

M. Knott; F. Perez-Cruz; T. Defraeye 

Journal Of Food Engineering. 2023-01-12. Vol. 345, p. 111401. DOI : 10.1016/j.jfoodeng.2022.111401.

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

L. D. Natale; Y. Lian; E. Maddalena; J. Shi; C. N. Jones 

2023-01-10. 2022 IEEE 61st Conference on Decision and Control (CDC). DOI : 10.1109/CDC51059.2022.9992445.

Bridging structural MRI with cognitive function for individual level classification of early psychosis via deep learning

Y. Wen; C. Zhou; L. Chen; Y. Deng; M. Cleusix et al. 

Frontiers In Psychiatry. 2023-01-10. Vol. 13, p. 1075564. DOI : 10.3389/fpsyt.2022.1075564.

Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units

P. Rokhforoz; M. Montazeri; O. Fink 

Reliability Engineering & System Safety. 2023-01-10. Vol. 232, p. 109081. DOI : 10.1016/j.ress.2022.109081.

Single-Molecule Peptide Identification Using Fluorescence Blinking Fingerprints

S. Puntener; P. Rivera-Fuentes 

Journal Of The American Chemical Society. 2023-01-05. DOI : 10.1021/jacs.2c12561.

Supervised learning and inference of spiking neural networks with temporal coding

A. Stanojevic / W. Gerstner; S. A. Wozniak (Dir.)  

Lausanne, EPFL, 2023. 

The Societal and Scientific Importance of Inclusivity, Diversity, and Equity in Machine Learning for Chemistry

D. Probst 

CHIMIA. 2023. Vol. 77, num. 1/2, p. 56. DOI : 10.2533/chimia.2023.56.

An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification

D. Probst 

Journal of Cheminformatics. 2023. Vol. 15, num. 113. DOI : 10.1186/s13321-023-00784-y.

The inductive bias of deep learning: Connecting weights and functions

G. Ortiz Jimenez / P. Frossard (Dir.)  

Lausanne, EPFL, 2023. 

Deep Learning for 3D Surface Modelling and Reconstruction

B. A. R. Guillard / P. Fua (Dir.)  

Lausanne, EPFL, 2023. 

A Robotic Surgery Platform for Automated Tissue Micromanipulation in Zebrafish Embryos

E. Özelçi; E. Etesami; L. A. Rohde; A. C. Oates; S. Sakar 

IEEE Robotics and Automation Letters. 2023.  p. 1-8. DOI : 10.1109/LRA.2023.3333690.

Challenges and approaches in bridging the biomimicry gap in biohybrid systems of fish and robots

V. Papaspyros / F. Mondada (Dir.)  

Lausanne, EPFL, 2023. 

Principles of Network Plasticity in Neocortical Microcircuits

A. Ecker / H. Markram; M. Reimann (Dir.)  

Lausanne, EPFL, 2023. 

Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning

N. Pekçokgüler / A. P. Burg; C. Dehollain (Dir.)  

Lausanne, EPFL, 2023. 

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

L. Petrini / M. Wyart (Dir.)  

Lausanne, EPFL, 2023. 

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

L. F. Krapp / M. Dal Peraro (Dir.)  

Lausanne, EPFL, 2023. 

Tracking Adaptation to Improve SuperPoint for 3D Reconstruction in Endoscopy

L. Barbed; J. Montiel; P. Fua; A. Murillo 

2023. 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), Vancouver, BC, Canada, October 8–12, 2023. p. 583–593. DOI : 10.1007/978-3-031-43907-0_56.

Instruction-Level Power Side-Channel Leakage Evaluation of Soft-Core CPUs on Shared FPGAs

O. Glamočanin; S. Shrivastava; J. Yao; N. Ardo; M. Payer et al. 

Journal of Hardware and Systems Security. 2023. DOI : 10.1007/s41635-023-00135-1.

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

X. Kuang; K. Yuan; B. Du; J. Yang 

2023-01-01. International Joint Conference on Neural Networks (IJCNN), Broadbeach, AUSTRALIA, Jun 18-23, 2023. DOI : 10.1109/IJCNN54540.2023.10191294.

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

X. Li; F. Marechal 

Energy And Ai. 2023-01-01. Vol. 11, p. 100219. DOI : 10.1016/j.egyai.2022.100219.

Label-free plasmonic microarray for multiplexed analysis of cells and tumor organoids

Y-C. Liu / H. Altug (Dir.)  

Lausanne, EPFL, 2023. 

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

S. Baghersalimi; T. Teijeiro; A. Aminifar; D. Atienza Alonso 

IEEE Transactions on Mobile Computing. 2023. 

Wind, Hail, and Climate Extremes: Modelling and Attribution Studies for Environmental Data

O. M. A. Miralles / A. C. Davison; V. Panaretos (Dir.)  

Lausanne, EPFL, 2023. 

Hardware-Software co-design Methodologies for Edge AI Optimization

F. Ponzina / D. Atienza Alonso (Dir.)  

Lausanne, EPFL, 2023. 

Benign Overfitting in Deep Neural Networks under Lazy Training

Z. Zhu; F. Liu; G. Chrysos; F. Locatello; V. Cevher 

2023. 40th International Conference on Machine Learning (ICML), Honolulu, Hawaii, USA, July, 23-29, 2023.

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

C. Karmann; B. Aydemir; K. Chamilothori; S. Kim; S. Süsstrunk et al. 

Journal of Environmental Psychology. 2023. Vol. 92, p. 102110. DOI : 10.1016/j.jenvp.2023.102110.

Interpretable statistical representations of neural population dynamics and geometry

A. Gosztolai; R. L. Peach; A. Arnaudon; M. Barahona; P. Vandergheynst 

arXiv. 2023. DOI : 10.48550/arxiv.2304.03376.

A Neural-Network-Based Convex Regularizer for Inverse Problems

A. Goujon; S. Neumayer; P. Bohra; S. Ducotterd; M. Unser 

IEEE Transactions on Computational Imaging. 2023. Vol. 9, p. 781-795. DOI : 10.1109/TCI.2023.3306100.

Exploring Novel Modalities for Optical Diffraction Tomography

A. Saba Shirvan / D. Psaltis (Dir.)  

Lausanne, EPFL, 2023. 

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

G. Ganeeva / C. Hébert; E. Oveisi (Dir.)  

Lausanne, EPFL, 2023. 

Noninvasive deep brain stimulation to modulate human behavior by means of transcranial temporal interference electrical stimulation

E. Beanato / F. C. Hummel (Dir.)  

Lausanne, EPFL, 2023. 

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

I. Kang; Y. Jiang; M. Holler; M. Guizar Sicairos; A. F. J. Levi et al. 

Optica. 2023. Vol. 10, num. 8, p. 1000-1008. DOI : 10.1364/OPTICA.492666.