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

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 ...

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 ...

Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to differentiate individuals and decode cognitive tasks. In this work, we use methods from graph structure inference to estimate FC, which is in c ...

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 ...

In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning. Statistical physics provides analytical tools to study fundamental limitations in their ...

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 ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing devia ...

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 ...

The dynamics and connectivity of neural circuits continuously change on timescales ranging from milliseconds to an animal's lifetime. Therefore, to understand biological networks, minimally invasive methods are required to repeatedly record them in behavin ...

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 ...

Neural functions operate in tightly controlled conditions that are mediated by multiple electrical and chemical phenomena. Brain disorders such as Parkinson's Disease and Alzheimer's Disease perturb these conditions and cause a loss of neurons, which impai ...

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

Chat with Graph Search

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

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

Joint Subject-Identification and Task-Decoding From Inferred Functional Brain Graphs Via a Multi-Task Neural Network

Smart Edge for Hierarchical Vision Systems

Sparse representations, inference and learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Probabilistic methods for neural combinatorial optimization

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

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

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

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

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

Improving K-means Clustering Using Speculation

Performance of Social Machine Learning under Limited Data

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Learning to Align Sequential Actions in the Wild

Microengineered devices enable long-term imaging of the ventral nerve cord in behaving adult Drosophila

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

A neural probe to sample brain fluid in nanoliter volumes for quantitative molecular studies