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Visar resultat 1 - 5 av 400 avhandlingar som matchar ovanstående sökkriterier.

1. 1. Towards Accurate and Reliable Deep Regression Models

   Författare :Fredrik K. Gustafsson; Thomas B. Schön; Martin Danelljan; Søren Hauberg; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine Learning; Deep Learning; Regression; Probabilistic Models; Energy-Based Models; Uncertainty Estimation; Machine learning; Maskininlärning;

   Sammanfattning : Regression is a fundamental machine learning task with many important applications within computer vision and other domains. In general, it entails predicting continuous targets from given inputs. LÄS MER

3. 2. Statistical inference with deep latent variable models

   Författare :Najmeh Abiri; Beräkningsbiologi och biologisk fysik - Genomgår omorganisation; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Deep Learning; Generative Models; Variational Inference; Missing data; Imputation; Fysicumarkivet A:2019:Abiri;

   Sammanfattning : Finding a suitable way to represent information in a dataset is one of the fundamental problems in Artificial Intelligence. With limited labeled information, unsupervised learning algorithms help to discover useful representations. LÄS MER

4. 3. Visual Representations and Models: From Latent SVM to Deep Learning

   Författare :Hossein Azizpour; Stefan Carlsson; Barbara Caputo; KTH; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Computer Vision; Machine Learning; Artificial Intelligence; Deep Learning; Learning Representation; Deformable Part Models; Discriminative Latent Variable Models; Convolutional Networks; Object Recognition; Object Detection; Computer Science; Datalogi;

   Sammanfattning : Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. LÄS MER

5. 4. Deep Perceptual Loss and Similarity

   Författare :Gustav Grund Pihlgren; Marcus Liwicki; Fredrik Sandin; Hugo Larochelle; Luleå tekniska universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Image Similarity; Perceptual Similarity; Perceptual Loss; Deep Features; Deep Learning; Machine Learning; Maskininlärning;

   Sammanfattning : This thesis investigates deep perceptual loss and (deep perceptual) similarity; methods for computing loss and similarity for images as the distance between the deep features extracted from neural networks. The primary contributions of the thesis consist of (i) aggregating much of the existing research on deep perceptual loss and similarity, and (ii) presenting novel research into understanding and improving the methods. LÄS MER

7. 5. Computational Models in Deep Brain Stimulation : Patient‐Specific Simulations, Tractography, and Group Analysis

   Författare :Teresa Nordin; Karin Wårdell; Simone Hemm-Ode; Johannes D. Johansson; Harith Akram; Linköpings universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Anisotropy; Deep brain stimulation DBS ; Finite element method FEM ; Patient‐specific simulation; Probabilistic stimulation maps PSM ; Tractography;

   Sammanfattning : Deep brain stimulation (DBS) is an established method for symptom relief in movement disorders like Parkinson’s disease, essential tremor (ET), and dystonia. The therapy is based on implanting an electrode with four contacts in the deep brain structures where it provides electrical stimulation, mainly impacting the nerve tracts. LÄS MER