This thesis explores the application of artificial intelligence, specifically using autoencoder (AE) and variational autoencoder (VAE), for anomaly detection, when dealing with data imbalance. The primary aim is to develop and evaluate models that can predict human errors during the assembly process of engine valves and springs, while addressing re...
In this article, we consider a system of parametric ODEs which involves unknown parameters and we seek to identify the values of the parameters associated to a given measurement. To do so, we place ourselves within the fairly usual framework that this single measurement is in fact taken from a population of data and we therefore want to take advant...
Autoencoders, a class of neural networks, have emerged as a valuable tool for anomaly detection and trajectory clustering: they produce a compressed latent space and capture essential features in the data. However, their lack of interpretability poses challenges in the context of ATM, where clear-cut explanations are crucial. In this paper, we inve...
This thesis is dedicated to the exploration and understanding of neural network latent spaces, to allow the creation of a link between the latter and classical structural descriptors to perform inverse QSAR. The generative potential of seq2seq architectures often comes with a blurry understanding of the rules governing its chemical spaces. A study ...
Published in Journal of Physics: Conference Series
A noticeable trend of machine learning is to deal with data of various modalities. Besides multimodal motivation, learning more from general information without forgetting the prior, or incremental learning, could also benefit unimodal machine learning. Human understanding often starts from a simplistic, generic view of the whole problem and then f...
Recent Sentinel satellite constellations and deep learning methods offer great possibilities for estimating the states and dynamics of physical parameters on a global scale. Such parameters and their corresponding uncertainties can be retrieved by machine learning methods solving probabilistic inverse problems. Nevertheless, the scarcity of referen...
Tato diplomová práce si klade za cíl analyzovat data pacientů s chronickými onemocněními a identifikovat podskupiny jejich populace, pro které lze poskytnout více personalizovanou léčbu pomocí nejmodernějších metod shlukování a hlubokého učení. Zaměřuje se dále na pacienty s chronickou závislostí na tabáku a poskytuje přehled nejnovějších přístupů ...
Anomaly detection of train wheels helps railway operators to find wheel defects and save cost by enabling condition-based maintenance. Existing approaches focus on applications on freight trains and use supervised data-driven methods which need substantial volume of fault data. This is often unavailable in passenger railways that normally provide r...
Published in IEEE transactions on pattern analysis and machine intelligence
A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning. However, these methods typically only provide an embedding of the input data and cannot exten...
Coherent anti-Stokes Raman scattering (CARS) microspectroscopy is a powerful tool for label-free cell imaging thanks to its ability to acquire a rich amount of information. An important family of operations applied to such data is multivariate curve resolution (MCR). It aims to find main components of a dataset and compute their spectra and concent...
