Publications associées à Sparse representations, inference and learning

Publications associées (20)

Development of a blind control occupant behaviour model (BC-OBM) based on contextual and time-related factors through supervised machine learning approaches

Blind use patterns significantly impact visual comfort and energy efficiency in office buildings. To quantify this impact on building performance, blind control occupant behaviour models (BC-OBMs) are typically integrated into building performance simulati ...

2025

Transfer learning-based approach for evaluating residual stiffness in carbon fiber reinforced composites and adhesives

Anastasios Vassilopoulos, Licai Cao

This work proposes a transfer learning-based encoder-decoder framework to predict the relationship between loading conditions and residual stiffness in carbon fiber reinforced composites and adhesives. The encoder, built from a Convolutional Neural Network ...

2025

Correlation of the L-mode density limit with edge collisionality

Alessandro Pau

The 'density limit' is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot ...

IOP Publishing Ltd2025

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Loris Di Natale

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

EPFL2024

Smart Edge for Hierarchical Vision Systems

Yamin Sepehri

Deep neural networks provide magnificent results for vision tasks. However, their complex structures and numerous parameters make them computationally and memory-wise intensive. Execution of these models on resource-constrained, camera-equipped devices suc ...

EPFL2024

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Seyed Mohammad Mahdi Johari

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

EPFL2024

Batch Universal Prediction

Michael Christoph Gastpar, Marco Bondaschi

Large language models (LLMs) have recently gained much popularity due to their surprising ability at generating human-like English sentences. LLMs are essentially predictors, estimating the probability of a sequence of words given the past. Therefore, it i ...

IEEE2024

Probabilistic methods for neural combinatorial optimization

Nikolaos Karalias

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

EPFL2023

Performance of Social Machine Learning under Limited Data

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

This paper studies the non-Asymptotic classification performance of the social machine learning strategy. This strategy involves an independent training phase followed by a cooperative inference phase to classify a growing number of samples. By considering ...

IEEE2023

Visual question answering from another perspective: CLEVR mental rotation tests *

Sina Honari

Different types of mental rotation tests have been used extensively in psychology to understand human visual reasoning and perception. Understanding what an object or visual scene would look like from another viewpoint is a challenging problem that is made ...

ELSEVIER SCI LTD2023

Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification

Matthias Rottmann

In this work, we present for the first time a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition for semantic segmentation datasets is time-consuming and requires plenty of ...

IEEE Computer Soc2023

Generating Controlled Physics-Informed Time-to-failure Trajectories for Prognostics in Unseen Operational Conditions

Olga Fink

Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Generating synthetic d ...

Research Publishing2023

Acoustic emission for the prediction of processing regimes in Laser Powder Bed Fusion, and the generation of processing maps

Roland Logé, Jamasp Jhabvala, Charlotte Léopoldine de Formanoir de la Cazerie, Rita Drissi Daoudi

The Laser Power Bed Fusion (LPBF) process is of high interest to many industries, such as motors and vehicles, robotics, biomedical applications, aerospace, and others. LPBF workpieces can indeed achieve near full density and high resistance. However, a la ...

ELSEVIER2023

Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding

Mutian He

Recently, large pretrained language models have demonstrated strong language understanding capabilities. This is particularly reflected in their zero-shot and in-context learning abilities on downstream tasks through prompting. To assess their impact on sp ...

Isca-Int Speech Communication Assoc2023

Improving K-means Clustering Using Speculation

Stefan Igescu, Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou

K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...

2023

The Timepix4 analog front-end design: Lessons learnt on fundamental limits to noise and time resolution in highly segmented hybrid pixel detectors

Jean-Michel Sallese, Adil Koukab

This manuscript describes the optimization of the front-end readout electronics for high granularity hybrid pixel detectors. The theoretical study aims at minimizing the noise and jitter. The model presented here is validated with both circuit post layout ...

2023

A hardware/software co-design vision for deep learning at the edge

David Atienza Alonso, Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Miguel Peon Quiros, Flavio Ponzina, Marco Antonio Rios, Benoît Walter Denkinger, Simone Machetti

The growing popularity of edgeAI requires novel solutions to support the deployment of compute-intense algorithms in embedded devices. In this article, we advocate for a holistic approach, where application-level transformations are jointly conceived with ...

2022

Impact of dipole-dipole interactions on motility-induced phase separation

Ignacio Pagonabarraga Mora, Elena Sesé Sansa

We present a hydrodynamic theory for systems of dipolar active Brownian particles which, in the regime of weak dipolar coupling, predicts the onset of motility-induced phase separation (MIPS), consistent with Brownian dynamics (BD) simulations. The hydrody ...

ROYAL SOC CHEMISTRY2022

Learning to Align Sequential Actions in the Wild

Pascal Fua, Bugra Tekin, Weizhe Liu

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspon- dences across videos in time. They either learn frame-to- frame mapping across sequences, which does not leverage temporal information, or a ...

IEEE COMPUTER SOC2022

Editorial: Computational Neuroimage Analysis Tools for Brain (Diseases) Biomarkers

Meritxell Bach Cuadra

FRONTIERS MEDIA SA2022