Publications associées à Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion

Publications associées (24)

Integrating Neural Interfaces with Machine Learning: Enabling Self-Sufficient and Ubiquitous Prosthetic and Diagnostic Devices

Mahsa Shoaran, Mohammad Ali Shaeri, Jinhan Liu

Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well as prosthetic devices (for motor and communication recovery). By integratin ...

IEEE2025

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Devis Tuia

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in datascarce regions. To address it we have develop ...

Springer Science and Business Media LLC2024

The multimodality cell segmentation challenge: toward universal solutions

Maxime Emmanuel Scheder, Jun Ma, Sahand Jamal Rahi, Xin Yang, Vojislav Gligorovski, Marco Labagnara

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Nature Publishing Group2024

On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Lloyd Haydn Hughes, Robin Adrien Zbinden

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited ...

Elsevier2024

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation

Auke Ijspeert, Henri Charles Alexandre Lorach, Alice Julie Bruel, Thibault Jean Etienne Collin, Icare Sakr

Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway a ...

Public Library Science2024

End-to-end kernel learning via generative random Fourier features

Fanghui Liu

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

ELSEVIER SCI LTD2023

Can Self-Supervised Neural Representations Pre-Trained on Human Speech distinguish Animal Callers?

Mathew Magimai Doss, Eklavya Sarkar

Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...

ISCA2023

Ageing-aware battery discharge prediction with deep learning

Olga Fink, Luca Biggio

Electrochemical batteries are ubiquitous devices in our society. When employed in mission-critical applications, the ability to precisely predict their end-of-discharge under highly variable operating conditions is of paramount importance in order to suppo ...

ELSEVIER SCI LTD2023

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

Berend Smit, Seyedmohamad Moosavi, Kevin Maik Jablonka, Christopher Patrick Ireland, Christoph Leopold Talirz, Fatmah Ebrahim, Nency Patricio Domingues

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of A ...

NATURE PORTFOLIO2022

Information theoretic limits of learning a sparse rule

Nicolas Macris, Clément Dominique Luneau

We consider generalized linear models in regimes where the number of nonzero components of the signal and accessible data points are sublinear with respect to the size of the signal. We prove a variational formula for the asymptotic mutual information per ...

IOP Publishing Ltd2022

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

Maximilian Wieland Hofer

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate thr ...

EPFL2022

Local invertibility and sensitivity of atomic structure-feature mappings

Michele Ceriotti, Sergey Pozdnyakov

Background: The increasingly common applications of machine-learning schemes to atomic-scale simulations have triggered efforts to better understand the mathematical properties of the mapping between the Cartesian coordinates of the atoms and the variety o ...

2021

Critical Parameters for Scalable Distributed Learning with Large Batches and Asynchronous Updates

Martin Jaggi, Sebastian Urban Stich, Amirkeivan Mohtashami

It has been experimentally observed that the efficiency of distributed training with stochastic gradient (SGD) depends decisively on the batch size and—in asynchronous implementations—on the gradient staleness. Especially, it has been observed that the spe ...

MICROTOME PUBLISHING2021

Improve the Model Stability of Dam's Displacement Prediction Using a Numerical-Statistical Combined Model

Zhenzhu Meng

In most studies of dam's displacement prediction based on monitoring data, emphasis was given on improving the prediction accuracy, while the model stability was merely considered. This study proposed a numerical-statistical combined model which aims to im ...

2020

Risk prediction error signaling: A two-component response?

Michael Herzog, Kerstin Preuschoff, Ophélie Gladys Favrod, Marc Michael Lauffs

Organisms use rewards to navigate and adapt to (uncertain) environments. Error-based learning about rewards issupported by the dopaminergic system, which is thought to signal reward prediction errors to make adjustmentsto past predictions. More recently, t ...

2020

Robust Distributed Learning

El Mahdi El Mhamdi

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...

EPFL2020

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Silvestro Micera, Fiorenzo Artoni

Objective.Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. ...

IOP PUBLISHING LTD2020

Fast Automatic Smoothing for Generalized Additive Models

Anthony Christopher Davison, Yousra El Bachir

Generalized additive models (GAMs) are regression models wherein parameters of probability distributions depend on input variables through a sum of smooth functions, whose degrees of smoothness are selected by L-2 regularization. Such models have become th ...

Microtome Publishing2019

SmarPer: Context-Aware and Automatic Runtime-Permissions for Mobile Devices

Jean-Pierre Hubaux, Kévin Clément Huguenin, Mohammad Emtiyaz Khan, Italo Ivan Dacosta Petrocelli, Joana Catarina Soares Machado, Katarzyna Lucja Olejnik

Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that oft ...

IEEE2017

Predictive models for assessing the passive solar and daylight potential of neighborhood designs: A comparative proof-of-concept study

Marilyne Andersen, Emmanuel Rey, Émilie Nault

Despite recent developments, neighborhood-scale performance assessment at the early-design phase is seldom carried out in practice, notably due to high computational complexity, time requirement, and perceived need for expert knowledge, ultimately limiting ...

2017