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.

2024

Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica

V. Tollenaar; H. Zekollari; F. Pattyn; M. Russwurm; B. Kellenberger et al. 

Geophysical Research Letters. 2024-02-16. Vol. 51, num. 3, p. e2023GL106285. DOI : 10.1029/2023GL106285.

Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

L. Leuzzi; M. Meneghetti; G. Angora; R. B. Metcalf; L. Moscardini et al. 

Astronomy & Astrophysics. 2024-01-16. Vol. 681, p. A68. DOI : 10.1051/0004-6361/202347244.

Dynamic Behavior of Spatially Confined Sn Clusters and Its Application in Highly Efficient Sodium Storage with High Initial Coulombic Efficiency

H. Ma; R. Yu; W. Xu; L. Zhang; J. Chen et al. 

Advanced Materials. 2024-01-15. DOI : 10.1002/adma.202307151.

Meta-learning to address diverse Earth observation problems across resolutions

M. Russwurm; S. Wang; B. A. Kellenberger; R. Roscher; D. Tuia 

Communications Earth & Environment. 2024-01-12. Vol. 5, num. 1, p. 37. DOI : 10.1038/s43247-023-01146-0.

JDLL: a library to run deep learning models on Java bioimage informatics platforms

C. G. L. de Haro; S. Dallongeville; T. Musset; E. Gomez-de-Mariscal; D. Sage et al. 

Nature Methods. 2024-01-08. DOI : 10.1038/s41592-023-02129-x.

Predicting protein interactions using geometric deep learning on protein surfaces

F. Sverrisson / B. E. Ferreira De Sousa Correia; M. Bronstein (Dir.)  

Lausanne, EPFL, 2024. 

Predicting the long-term collective behaviour of fish pairs with deep learning

V. Papaspyros; R. Escobedo; A. Alahi; G. Theraulaz; C. Sire et al. 

Journal of The Royal Society Interface. 2024. Vol. 21, num. 212. DOI : 10.1098/rsif.2023.0630.

Understanding and Enhancing Digital Wellbeing Dimensions: Empirical Studies on Smartphone Usage Patterns and Recommendations of Micro-Informative Content

R. Islambouli / D. Gillet; S. Ingram (Dir.)  

Lausanne, EPFL, 2024. 

Cellpose training data and scripts from “Inhibition of CERS1 in aging skeletal muscle exacerbates age-related muscle impairments”

M. Wohlwend; O. Burri; J. Auwerx 

2024.

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

P. Grönquist; Y. Ren; Q. He; A. Verardo; S. Süsstrunk 

2024. Electronic Imaging Media Watermarking, Security, and Forensics, Californie, USA, Janvier, 21-25, 2024.

Computational drug development for membrane protein targets

H. Li; X. Sun; W. Cui; M. Xu; J. Dong et al. 

Nature Biotechnology. 2024. Vol. 42, num. 2, p. 229-242. DOI : 10.1038/s41587-023-01987-2.

Towards Trustworthy Deep Learning for Image Reconstruction

A. M. F. Goujon / M. Unser (Dir.)  

Lausanne, EPFL, 2024. 

Towards the Detection of AI-Synthesized Human Face Images

Y. Lu; T. Ebrahimi 

2024. 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, October 27-30, 2024.

Assessment framework for deepfake detection in real-world situations

Y. Lu; T. Ebrahimi 

EURASIP Journal on Image and Video Processing. 2024. Vol. 2024, num. 1. DOI : 10.1186/s13640-024-00621-8.

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

M. Atzeni / P. Vandergheynst; A. Loukas (Dir.)  

Lausanne, EPFL, 2024. 

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

L. Di Natale / C. N. Jones; B. Svetozarevic (Dir.)  

Lausanne, EPFL, 2024. 

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (6/6)

R. Vinals Terres; J-P. Thiran 

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

R. Vinals Terres; J-P. Thiran 

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

R. Vinals Terres; J-P. Thiran 

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

R. Vinals Terres; J-P. Thiran 

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

R. Vinals Terres; J-P. Thiran 

2024.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (1/6)

R. Vinals Terres; J-P. Thiran 

2024.

Safe Deep Neural Networks

K. M. Matoba / P. Vandergheynst; F. Fleuret (Dir.)  

Lausanne, EPFL, 2024. 

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

I. Nejjar; F. Geissmann; M. Zhao; C. Taal; O. Fink 

Reliability Engineering & System Safety. 2024. Vol. 242, p. 109718. DOI : 10.1016/j.ress.2023.109718.

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

H. Lu; A. Thelen; O. Fink; C. Hu; S. Laflamme 

Mechanical Systems and Signal Processing. 2024. Vol. 210, p. 111068. DOI : 10.1016/j.ymssp.2023.111068.

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

E. P. G. Courdier / F. Fleuret; A. Roshan Zamir (Dir.)  

Lausanne, EPFL, 2024. 

Aggregating Spatial and Photometric Context for Photometric Stereo

D. Honzátko / P. Fua; E. Türetken (Dir.)  

Lausanne, EPFL, 2024. 

Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications

J. A. H. Klein / D. Atienza Alonso; M. Zapater Sancho (Dir.)  

Lausanne, EPFL, 2024. 

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

L. B. Meegahapola / D. Gatica-Perez (Dir.)  

Lausanne, EPFL, 2024. 

Incorporating Projective Geometry into Deep Learning

M. J. Tyszkiewicz / P. Fua (Dir.)  

Lausanne, EPFL, 2024. 

Random matrix methods for high-dimensional machine learning models

A. P. M. Bodin / N. Macris (Dir.)  

Lausanne, EPFL, 2024. 

3D diffractive optics for linear interconnects and nonlinear processing

N. U. Dinç / D. Psaltis; C. Moser (Dir.)  

Lausanne, EPFL, 2024. 

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/j.cam.2023.115667.

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. 2024-01-01. Vol. 9, p. 327-334. DOI : 10.1109/LRA.2023.3333690.

2023

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

S. Graham; Q. D. Vu; M. Jahanifar; M. Weigert; U. Schmidt et al. 

Medical Image Analysis. 2023-12-28. Vol. 92, p. 103047. DOI : 10.1016/j.media.2023.103047.

Reinforcement bond performance in 3D concrete printing: Explainable ensemble learning augmented by deep generative adversarial networks

X. Wang; N. Banthia; D-Y. Yoo 

Automation In Construction. 2023-12-19. Vol. 158, p. 105164. DOI : 10.1016/j.autcon.2023.105164.

Fixing the problems of deep neural networks will require better training data and learning algorithms

J. S. Bowers; G. Malhotra; M. Dujmovic; M. L. Montero; C. Tsvetkov et al. 

Behavioral And Brain Sciences. 2023-12-06. Vol. 46, p. e400. DOI : 10.1017/S0140525X23001589.

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

C. F. Park; M. Barzegar-Keshteli; K. Korchagina; A. Delrocq; V. Susoy et al. 

Nature Methods. 2023-12-05. Vol. 21, num. 1. DOI : 10.1038/s41592-023-02096-3.

How deep convolutional neural networks lose spatial information with training

U. M. Tomasini; L. Petrini; F. Cagnetta; M. Wyart 

Machine Learning-Science And Technology. 2023-12-01. Vol. 4, num. 4, p. 045026. DOI : 10.1088/2632-2153/ad092c.

Reply to Comment on ‘Physics-based representations for machine learning properties of chemical reactions’

P. E. Van Gerwen; M. D. Wodrich; R. Laplaza; C. Corminboeuf 

Machine Learning-Science And Technology. 2023-12-01. Vol. 4, num. 4, p. 048002. DOI : 10.1088/2632-2153/acee43.

Euclid: Identification of asteroid streaks in simulated images using deep learning

M. Pontinen; M. Granvik; A. A. Nucita; L. Conversi; B. Altieri et al. 

Astronomy & Astrophysics. 2023-11-29. Vol. 679, p. A135. DOI : 10.1051/0004-6361/202347551.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening

B. Chen; Z. Liu; J. Lu; Z. Li; K. Kuang et al. 

Respiratory Research. 2023-11-28. Vol. 24, num. 1, p. 299. DOI : 10.1186/s12931-023-02611-2.

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

R. Vinals Terres; J-P. Thiran 

Journal of Imaging. 2023-11-23. Vol. 9, num. 12, p. 256. DOI : 10.3390/jimaging9120256.

Bed Topography Inference from Velocity Field Using Deep Learning.

M. Kiani Oshtorjani; C. Ancey 

Water. 2023-11-22. Vol. 15, p. 4055. DOI : 10.3390/w15234055.

Electricity Theft Detection Using Dynamic Graph Construction and Graph Attention Network

W. Liao; R. Zhu; Z. Yang; K. Liu; B. Zhang et al. 

Ieee Transactions On Industrial Informatics. 2023-11-20. DOI : 10.1109/TII.2023.3331131.

A new age in protein design empowered by deep learning

H. Khakzad; I. Igashov; A. Schneuing; C. Goverde; M. Bronstein et al. 

Cell Systems. 2023-11-15. Vol. 14, num. 11, p. 925-939. DOI : 10.1016/j.cels.2023.10.006.

Monotonicity Reasoning in the Age of Neural Foundation Models

Z. Chen; Q. Gao 

Journal Of Logic Language And Information. 2023-11-15. DOI : 10.1007/s10849-023-09411-3.

Rethinking data augmentation for adversarial robustness

H. Eghbal-zadeh; W. Zellinger; M. Pintor; K. Grosse; K. Koutini et al. 

Information Sciences. 2023-11-07. Vol. 654, num. 119838, p. 1-17. DOI : 10.1016/j.ins.2023.119838.

Efficient approximation of cardiac mechanics through reduced-order modeling with deep learning-based operator approximation

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

International Journal For Numerical Methods In Biomedical Engineering. 2023-11-03. Vol. 40, num. 1. DOI : 10.1002/cnm.3783.

Signal domain adaptation network for limited-view optoacoustic tomography

A. K. Susmelj; B. Lafci; F. Ozdemir; N. Davoudi; X. L. Dean-Ben et al. 

Medical Image Analysis. 2023-11-01. Vol. 91, p. 103012. DOI : 10.1016/j.media.2023.103012.

Learning sparse features can lead to overfitting in neural networks

L. Petrini; F. Cagnetta; E. Vanden-Eijnden; M. Wyart 

Journal Of Statistical Mechanics-Theory And Experiment. 2023-11-01. Vol. 2023, num. 11, p. 114003. DOI : 10.1088/1742-5468/ad01b9.

Multi-layer state evolution under random convolutional design

M. Daniels; C. Gerbelot; F. Krzakala; L. Zdeborova 

Journal Of Statistical Mechanics-Theory And Experiment. 2023-11-01. Vol. 2023, num. 11, p. 114002. DOI : 10.1088/1742-5468/ad0220.

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.

Noninvasive theta-burst stimulation of the human striatum enhances striatal activity and motor skill learning

M. J. Wessel; E. Beanato; T. Popa; F. Windel; P. Vassiliadis et al. 

Nature Neuroscience. 2023-10-19. Vol. 26, num. 11. DOI : 10.1038/s41593-023-01457-7.

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

R. Romijnders; F. Salis; C. Hansen; A. Kuederle; A. Paraschiv-Ionescu et al. 

Frontiers In Neurology. 2023-10-16. Vol. 14, p. 1247532. DOI : 10.3389/fneur.2023.1247532.

AI-based forecasting for optimised solar energy management and smart grid efficiency

P. Bouquet; I. Jackson; M. Nick; A. Kaboli 

International Journal Of Production Research. 2023-10-14. DOI : 10.1080/00207543.2023.2269565.

Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma

J. Sun; L. Zhang; B. Hu; Z. Du; W. C. Cho et al. 

Lung Cancer. 2023-10-09. Vol. 186, p. 107392. DOI : 10.1016/j.lungcan.2023.107392.

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.

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Z. Jin; B. Noh 

Electronics. 2023-10-01. Vol. 12, num. 20, p. 4335. DOI : 10.3390/electronics12204335.

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.

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

S. J. Anagnostopoulos; J. Bauer; M. C. A. Clare; M. D. Piggott 

Renewable Energy. 2023-09-28. Vol. 218, p. 119293. DOI : 10.1016/j.renene.2023.119293.

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.

Artificial intelligence for natural product drug discovery

M. W. Mullowney; K. R. Duncan; S. S. Elsayed; N. Garg; J. J. J. van der Hooft et al. 

Nature Reviews Drug Discovery. 2023-09-11. DOI : 10.1038/s41573-023-00774-7.

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 

2023-09-08.

Reconstruction of decays to merged photons using end-to-end deep learning with domain continuation in the CMS detector

A. Tumasyan; W. Adam; J. W. Andrejkovic; T. Bergauer; S. Chatterjee et al. 

Physical Review D. 2023-09-05. Vol. 108, num. 5, p. 052002. DOI : 10.1103/PhysRevD.108.052002.

Sensor-agnostic Deep Learning of Coastal Ocean Chlorophyll-a

L. Hughes; D. Tuia; M. Smith; L. Lain 

2023-09-01. Trevor Platt Science Foundation (TPSF) conference, Plymouth, UK, August 9-11, 2023.

A deep learning-approach to infer somatic alterations in glioma using digital tissue slides

B. Kriener; S. Mueller; S. M. Waszak 

2023-09-01. 20th International Congress of Neuropathology, Berlin, GERMANY, SEP 13-16, 2023.

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 

2023-08-18.

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 

2023-06-30.

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.

Meteor: Meta-learning connecting earth problems observed from space

M. C. Russwurm; R. Roscher; B. A. Kellenberger; S. Wang; D. Tuia 

2023-06-01. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Vancouver, CA, June 18-22, 2023.

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. Vol. 62, num. 21, p. e202301963. 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.

20230901

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

R. P. Theiler; M. Viscione; O. Fink 

20230901. The 33rd European Safety and Reliability Conference (ESREL 2023), Southampton, UK, September 3-7,2023. p. 2419-2420. DOI : 10.3850/978-981-18-8071-1.