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

Related publications (23)

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

Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

José Angel Miranda Calero

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

IEEE2025

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

2025

Accelerating MoE Model Inference with Expert Sharding

André Loureiro Espírito Santo, Anne-Marie Kermarrec, Rafael Pereira Pires, Marinus Abraham de Vos, Milos Vujasinovic

Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communication overhead. While prior work has focused on optimizing MoE training and d ...

Association for Computing Machinery2025

Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

Stefan Christian Peters, Martin Vetterli, Eric Bezzam

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interfe ...

IEEE2025

Resource-efficient photonic networks for next-generation AI computing

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

Current trends in artificial intelligence toward larger models demand a rethinking of both hardware and algorithms. Photonics-based systems offer high-speed, energy-efficient computing units, provided algorithms are designed to exploit photonics' unique st ...

SPRINGERNATURE2025

Label Critic: Design Data Before Models

Andrea Cavalli

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that p ...

IEEE Computer Society2025

Generative spatial artificial intelligence for sustainable smart cities: A pioneering large flow model for urban digital twin

Paul Erich Keel, Jeffrey Huang, Simon Elias Bibri

Rapid urbanization, alongside escalating resource depletion and ecological degradation, underscores the critical need for innovative urban development solutions. In response, sustainable smart cities are increasingly turning to cutting-edge technologies—su ...

2025

Finding gravitational lenses at the dawn of the new generation of wide-field surveys

Javier Alejandro Acevedo Barroso

Strong gravitational lensing has become one of the main astrophysical probes of the extragalactic universe. It allows precise measurements of deflector galaxy masses. It acts as a cosmic telescope, revealing sources otherwise too dim or small to study. Whe ...

EPFL2025

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

Jürg Alexander Schiffmann, Soheyl Massoudi, Timothy Horvath, Joseph Bejjani, Dogukan Üstün

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural ...

ASME International2025

Optimal Inference in Contextual Stochastic Block Models

Lenka Zdeborová, Odilon Marie Cyprien Duranthon

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Journal of Machine Learning Research Inc.2024

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Yoeri Renier Johannes Poels

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles ...

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

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

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

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Rachid Guerraoui, John Stephan

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

Association for Computing Machinery, Inc2024

SAMPa: Sharpness-aware Minimization Parallelized

Volkan Cevher, Thomas Michaelsen Pethick, Wanyun Xie

Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires sequentially computing two gradients, effectively doubling the per-iteration cost compared to base optimizers like SGD. We ...

2024

Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions

Olga Fink, Han Sun

Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition ...

PHM Society2024

Multi-motif scaffolding for de novo pathogen antigen mimetics by deep generative learning

Karla Montserrat Castro Gilabert

Proteins are macromolecular machines central to virtually every fundamental biological function. Three-dimensional protein structures, determined by their amino acid composition, are directly linked to their function. Research into structure-function relat ...

EPFL2024