Sökning: "Deep averaging networks."
Visar resultat 1 - 5 av 7 uppsatser innehållade orden Deep averaging networks..
1. Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction
Master-uppsats, KTH/Matematisk statistikSammanfattning : Cone Beam Computed Tomography is a technology to visualize the 3D interior anatomy of a patient. It is important for image-guided radiation therapy in cancer treatment. During a scan, iterative methods are often used for the image reconstruction step. LÄS MER
2. Neural Networks for Modeling of Electrical Parameters and Losses in Electric Vehicle
Magister-uppsats, Högskolan i Skövde/Institutionen för ingenjörsvetenskapSammanfattning : Permanent magnet synchronous machines have various advantages and have showed the most superiorperformance for Electric Vehicles. However, modeling them is difficult because of their nonlinearity. LÄS MER
3. Active Learning for Extractive Question Answering
Master-uppsats, Linköpings universitet/Statistik och maskininlärningSammanfattning : Data labelling for question answering tasks (QA) is a costly procedure that requires oracles to read lengthy excerpts of texts and reason to extract an answer for a given question from within the text. QA is a task in natural language processing (NLP), where a majority of recent advancements have come from leveraging the vast corpora of unlabelled and unstructured text available online. LÄS MER
4. Classifying and Comparing Latent Space Representation of Unstructured Log Data.
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : This thesis explores and compares various methods for producing vector representation of unstructured log data. Ericsson wanted to investigate machine learning methods to analyze logs produced by their systems to reduce the cost and effort required for manual log analysis. LÄS MER
5. Federated Neural Collaborative Filtering for privacy-preserving recommender systems
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för systemteknikSammanfattning : In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. LÄS MER