Publications associées à DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Publications associées (16)

Automated Ensemble Multimodal Machine Learning for Healthcare

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stan ...

2025

An integrative computational modeling approach for Drosophila motor control

Pembe Gizem Ozdil

Animal motor control arises from the intricate interplay between neural circuits, biomechanics, and sensory feedback. Deciphering how these components interact is essential for uncovering the fundamental principles of movement generation. This dissertation ...

EPFL2025

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Devavrat Tomar

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

EPFL2025

Multi-Channel Mosra: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and A Teacher Model

Jozef Marus Coldenhoff

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device select ...

IEEE2024

Backpropagation-free Training of Analog AI Accelerators

Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Institute of Electrical and Electronics Engineers Inc.2024

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Thomas Yu

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

IEEE Computer Society2024

Finding Paths for Explainable MOOC Recommendation: A Learner Perspective

Marta Knezevic, Tanja Christina Käser Jacober, Jibril Albachir Frej, Tatjana Nazaretsky

The increasing availability of Massive Open Online Courses (MOOCs) has created a necessity for personalized course recommendation systems. These systems often combine neural networks with Knowledge Graphs (KGs) to achieve richer representations of learners ...

Assoc Computing Machinery2024

Mesh Neural Cellular Automata

Sabine Süsstrunk, Tong Zhang, Ehsan Pajouheshgar, Yitao Xu

Texture modeling and synthesis are essential for enhancing the realism of virtual environments. Methods that directly synthesize textures in 3D offer distinct advantages to the UV-mapping-based methods as they can create seamless textures and align more cl ...

Association for Computing Machinery (ACM)2024

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Sandro Carrara, Francesca Rodino, Lin Du

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

Pergamon-Elsevier Science Ltd2024

On the Convergence of Encoder-only Shallow Transformers

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu

In this paper, we aim to build the global convergence theory of encoder-only shallow Transformers under a realistic setting from the perspective of architectures, initialization, and scaling under a finite width regime. The difficulty lies in how to tackle ...

2023

Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models

Dolaana Khovalyg, Arnab Chatterjee

Dynamic human energy expenditure (EE) consists of energy costs for resting metabolism, food digestion, physical activity, and thermoregulation. Currently, multiple models predict EE mainly concerning physical activity, thus, discarding other factors contri ...

ELSEVIER SCI LTD2023

Euclid preparation XXV. The Euclid Morphology Challenge: Towards model-fitting photometry for billions of galaxies

Frédéric Courbin, Georges Meylan

The European Space Agency's Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment o ...

EDP SCIENCES S A2023

Deep Polynomial Neural Networks

Grigorios Chrysos

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their buil ...

2021

3D model discretization in assessing urban solar potential: the effect of grid spacing on predicted solar irradiation

Marilyne Andersen, Emmanuel Rey, Giuseppe Peronato

The increasing interest in solar energy production in urban areas requires an accurate simulation of solar irradiation on building surfaces, including vertical surfaces. However, solar potential analyses are usually conducted on 2.5D models, which are limi ...

2018

Learning Robot Gait Stability using Neural Networks as Sensory Feedback Function for Central Pattern Generators

Auke Ijspeert, José Santos-Victor, Sébastien Gay

In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we us ...

2013

Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

Ian Smith, Bernd Domer, Etienne Fest

2003