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.


Shape Holomorphy of Boundary Integral Operators on Multiple Open Arcs

J. Pinto; F. Henriquez; C. Jerez-Hanckes 

Journal Of Fourier Analysis And Applications. 2024-04-01. Vol. 30, num. 2, p. 14. DOI : 10.1007/s00041-024-10071-5.

“But of course i’m going to look happy” or “He needed to know I was angry”? Comparing use of emotional labour in teamwork in engineering and hospitality students

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

2024-03-28. EAPRIL 2023 Conference, Belfast, Northern Ireland (UK), November 22 – 24, 2023. p. 131-143.

The multimodality cell segmentation challenge: toward universal solutions

J. Ma; R. Xie; S. Ayyadhury; C. Ge; A. Gupta et al. 

Nature Methods. 2024-03-26. DOI : 10.1038/s41592-024-02233-6.

Can Gas Consumption Data Improve the Performance of Electricity Theft Detection?

W. Liao; R. Zhu; T. Ishizaki; Y. Li; Y. Jia et al. 

Ieee Transactions On Industrial Informatics. 2024-03-18. DOI : 10.1109/TII.2024.3371991.

Scalable semantic 3D mapping of coral reefs with deep learning

J. Sauder; G. Banc-Prandi; A. Meibom; D. Tuia 

Methods In Ecology And Evolution. 2024-03-14. DOI : 10.1111/2041-210X.14307.

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

H. Park; S. Majumdar; X. Zhang; J. Kim; B. Smit 

Digital Discovery. 2024-03-12. DOI : 10.1039/d4dd00010b.

On learning latent dynamics of the AUG plasma state

A. Kit; A. E. Jarvinen; Y. R. J. Poels; S. Wiesen; V. Menkovski et al. 

Physics Of Plasmas. 2024-03-01. Vol. 31, num. 3, p. 032504. DOI : 10.1063/5.0174128.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

A. Mueller; D. Schmidt; J. P. Albrecht; L. Rieckert; M. Otto et al. 

Nature Protocols. 2024-02-29. DOI : 10.1038/s41596-024-00957-5.

Live-cell imaging powered by computation

H. Shroff; I. Testa; F. Jug; S. Manley 

Nature Reviews Molecular Cell Biology. 2024-02-20. DOI : 10.1038/s41580-024-00702-6.

Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN

Y. Rosen; M. Brbic; Y. Roohani; K. Swanson; Z. Li et al. 

Nature Methods. 2024-02-16. DOI : 10.1038/s41592-024-02191-z.

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.

Deep learning approach for identification of H II regions during reionization in 21-cm observations – II. Foreground contamination

M. Bianco; S. K. Giri; D. Prelogovic; T. Chen; F. G. Mertens et al. 

Monthly Notices Of The Royal Astronomical Society. 2024-02-07. Vol. 528, num. 3, p. 5212-5230. DOI : 10.1093/mnras/stae257.

Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks

J. Qian; Y. Jiang; X. Liu; Q. Wang; T. Wang et al. 

Ieee Internet Of Things Journal. 2024-02-01. Vol. 11, num. 3, p. 5511-5525. DOI : 10.1109/JIOT.2023.3306826.

Butterfly effects in perceptual development: A review of the ‘adaptive initial degradation’ hypothesis

L. Vogelsang; M. Vogelsang; G. Pipa; S. Diamond; P. Sinha 

Developmental Review. 2024-01-19. Vol. 71, p. 101117. DOI : 10.1016/j.dr.2024.101117.

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.

Glenohumeral joint force prediction with deep learning

P. Eghbali; F. Becce; P. Goetti; P. Buchler; D. P. Pioletti et al. 

Journal Of Biomechanics. 2024-01-15. Vol. 163, p. 111952. DOI : 10.1016/j.jbiomech.2024.111952.

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.

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

V. Zermatten; X. Lu; J. Castillo-Navarro; T. Kellenberger; D. Tuia 

Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing. 2024-01-01. Vol. 17, p. 6468-6477. DOI : 10.1109/JSTARS.2024.3369876.

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

P. N. Bohra / M. Unser (Dir.)  

Lausanne, EPFL, 2024. 

Network time series forecasting in photovoltaics power production

J. Simeunovic / P. Frossard; R. E. Carrillo Rangel (Dir.)  

Lausanne, EPFL, 2024. 

Beyond fine-tuning: LoRA modules boost near-OOD detection and LLM security

E. Salimbeni; F. Craighero; R. Khasanova; M. Vasic; P. Vandergheynst 

2024. 7th Deep Learning Security and Privacy Workshop, San Francisco, CA, May 23, 2024.

Optimizing Dynamic Aperture Studies with Active Learning

D. Di Croce; M. Giovannozzi; E. Krymova; T. Pieloni; S. Redaelli et al. 

2024. DOI : 10.48550/arxiv.2402.11077.

RibSeg v2: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

L. Jin; S. Gu; D. Wei; J. K. Adhinarta; K. Kuang et al. 

Ieee Transactions On Medical Imaging. 2024-01-01. Vol. 43, num. 1, p. 570-581. DOI : 10.1109/TMI.2023.3313627.

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 


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 


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

R. Vinals Terres; J-P. Thiran 


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

R. Vinals Terres; J-P. Thiran 


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

R. Vinals Terres; J-P. Thiran 


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

R. Vinals Terres; J-P. Thiran 


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

R. Vinals Terres; J-P. Thiran 


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/

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.


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/

Deep Learning-Assisted Single-Molecule Detection of Protein Post-translational Modifications with a Biological Nanopore

C. Cao; P. Magalhaes; L. F. Krapp; J. F. B. Juarez; S. F. Mayer et al. 

Acs Nano. 2023-12-19. Vol. 18, num. 2, p. 1504-1515. DOI : 10.1021/acsnano.3c08623.

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.

Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

T. Stegmueller; C. R. Abbet; B. Bozorgtabar; H. Clarke; P. Petignat et al. 

Computers In Biology And Medicine. 2023-12-18. Vol. 169, p. 107809. DOI : 10.1016/j.compbiomed.2023.107809.

Boosting Weakly Convex Ridge Regularizers with Spatial Adaptivity

S. J. Neumayer; M. Pourya; A. Goujon; M. Unser 

2023-12-16. Fourth Workshop on Deep Learning and Inverse Problems (NeurIPS’23), New Orleans LA, USA, December 16, 2023.

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.

Using deep learning-based approaches to characterise ageing in different tissue components of normal breast tissue of women with different risk of developing breast cancer

S. Y. Chen; M. P. Centeno; G. Verghese; G. Booker; I. Wall et al. 

2023-12-01. 18th Annual Meeting of the European-Association-of-Neuro-Oncology (EANO), Rotterdam, NETHERLANDS, SEP 21-24, 2023. p. S13-S13.

Multi-site, Multi-domain Airway Tree Modeling

M. Zhang; Y. Wu; H. Zhang; Y. Qin; H. Zheng et al. 

Medical Image Analysis. 2023-12-01. Vol. 90, p. 102957. DOI : 10.1016/

Divergences in color perception between deep neural networks and humans

E. O. Nadler; E. Darragh-Ford; B. S. Desikan; C. Conaway; M. Chu et al. 

Cognition. 2023-12-01. Vol. 241, p. 105621. DOI : 10.1016/j.cognition.2023.105621.

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/

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.

Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection

Y. Che; Y. Zheng; F. E. Forest; X. Sui; X. Hu et al. 

Reliability Engineering & System Safety. 2023-09-08. Vol. 241, p. 109603. DOI : 10.1016/j.ress.2023.109603.

Learning-based techniques for lensless reconstruction

Y. Perron 


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.

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

R. P. Theiler; M. Viscione; O. Fink 

2023-09-01. 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.

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.