Publications associées à Unified Neural Encoding of BTFs

Publications associées (49)

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

Ramin Mohammadi, Nikolaos Stergiopoulos, Georgios Rovas, Lydia Aslanidou, Sokratis Anagnostopoulos

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

2025

t-PiNet: A thermodynamics-informed hierarchical learning for discovering constitutive relations of geomaterials

Konstantinos Karapiperis

More attention has been paid to integrating existing knowledge with data to understand the complex mechanical behaviour of geomaterials, but it incurs scepticism and criticism on its generalizability and robustness. Moreover, a common mistake in current da ...

2025

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

Alfio Quarteroni

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

2025

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Su Yeon Chang

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer ...

IOP Publishing Ltd2025

TimEHR: Image-based Time Series Generation for Electronic Health Records

David Atienza Alonso, Anisoara Ionescu, Hojjat Karami

Time series in Electronic Health Records (EHRs) present unique challenges for generative models, such as irregular sampling, missing values, and high dimensionality. In this paper, we propose a novel generative adversarial network (GAN) model, TimEHR, to g ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Machine Learning Applications to Conditional Asset Pricing: Attention, CNNs, and Neural Tangent Kernels

Boris Kuznetsov

The first two chapters develop ML-based models that generate conditional SDF estimates efficiently using conditioning information sets, resulting in minimal pricing errors and superior Sharpe ratios compared to traditional conditional asset pricing models. ...

EPFL2025

Slope Deformation Prediction Combining Particle Swarm Optimization-Based Fractional-Order Grey Model and K-Means Clustering

Sen Zheng

Slope deformation poses significant risks to infrastructure, ecosystems, and human safety, making early and accurate predictions essential for mitigating slope failures and landslides. In this study, we propose a novel approach that integrates a fractional ...

2025

Diffusion Stochastic Optimization for Min-Max Problems

Ali H. Sayed, Haoyuan Cai

The optimistic gradient method is useful in addressing minimax optimization problems. Motivated by the observation that the conventional stochastic version suffers from the need for a large batch size on the order of O(ϵ-2) to achieve an ϵ-stationary solut ...

2025

Harnessing deep learning to detect bronchiolitis obliterans syndrome from chest CT

Pascal Fua, Michele De Palma, Nahal Mansouri, Jakub Jan Gwizdala

BackgroundBronchiolitis Obliterans Syndrome (BOS), a fibrotic airway disease that may develop after lung transplantation, conventionally relies on pulmonary function tests (PFTs) for diagnosis due to limitations of CT imaging. Deep neural networks (DNNs) h ...

SPRINGERNATURE2025

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

IEEE2024

Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Jérôme Chenal, Baraka Jean-Claude Munyaka

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

AIP Publishing2025

Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Nature Portfolio2024

Perspective on nonvolatile magnon-signal storage and in-memory computation for low-power consuming magnonics

Arnaud Emile S. Nizet, Dirk Grundler, Andrea Mucchietto, Mingran Xu, Shreyas Sanjay Joglekar

Magnons are the quanta of spin waves and transport angular momenta through magnetically ordered materials. They can be used to distribute and control on-chip GHz signals without charge flow, thereby avoiding Joule heating. Beyond multiplexed signal process ...

AIP Publishing2025

The neural correlates of topographical disorientation-a lesion analysis study

Olaf Blanke, Lukas Heydrich, Eva Blondiaux

Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...

Wiley2024

Self-Supervised Anomaly Segmentation via Diffusion Models with Dynamic Transformer UNet

Seyedbehzad Bozorgtabar

A robust anomaly detection mechanism should possess the capability to effectively remediate anomalies, restoring them to a healthy state, while preserving essential healthy information. Despite the efficacy of existing generative models in learning the und ...

IEEE2025

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

Enhancing skin lesion classification: a CNN approach with human baseline comparison

Zaffar Ahmed Shaikh

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high ...

PeerJ2025

Robust machine learning for neuroscientific inference

Steffen Schneider

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

EPFL2024

On the added value of sequential deep learning for the upscaling of evapotranspiration

Devis Tuia

Estimating ecosystem-atmosphere fluxes such as evapotranspiration (ET) in a robust manner and at a global scale remains a challenge. Methods based on machine learning (ML) have shown promising results in achieving such upscaling, providing a complementary ...

2025

Neural Garment Modeling

Ren Li

The digitization of 3D deformable objects remains a significant challenge in computer graphics and vision, particularly in the accurate modeling of garments. Garments exhibit complex shape variability, non-rigid deformations, and frequent self-occlusion, m ...

EPFL2025