Sökning: "AutoEncoder"
Visar resultat 1 - 5 av 199 uppsatser innehållade ordet AutoEncoder.
1. Audio Anomaly Detection in Cars
Master-uppsats, Göteborgs universitet/Institutionen för matematiska vetenskaperSammanfattning : Audio anomaly detection in the context of car driving is a crucial task for ensuring vehicle safety and identifying potential faults. This paper aims to investigate and compare different methods for unsupervised audio anomaly detection using a data set consisting of recorded audio data from fault injections and normal "no fault" driving. LÄS MER
2. Modulating Depth Map Features to Estimate 3D Human Pose via Multi-Task Variational Autoencoders
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Human pose estimation (HPE) constitutes a fundamental problem within the domain of computer vision, finding applications in diverse fields like motion analysis and human-computer interaction. This paper introduces innovative methodologies aimed at enhancing the accuracy and robustness of 3D joint estimation. LÄS MER
3. Exploring the Applications of Machine Learning in the Public Sector
Kandidat-uppsats, Lunds universitet/Fysiska institutionen; Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationSammanfattning : Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. LÄS MER
4. Unsupervised Online Anomaly Detection in Multivariate Time-Series
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/DatorteknikSammanfattning : This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. LÄS MER
5. Autoencoder-Based Likelihood-Free Parameter Inference of Gene Regulatory Network
Master-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Likelihood-free parameter inference is a well-known statistical methodology that estimates the posterior distribution of model parameters even in cases where the likelihood function is intractable. The performance of this method is highly correlated with the learning of summary statistics, which capture the key features from the high dimensional data such as time series. LÄS MER