Sökning: "Deep Clustering"

Visar resultat 1 - 5 av 27 avhandlingar innehållade orden Deep Clustering.

1. 1. Deep learning for news topic identification in limited supervision and unsupervised settings

   Författare :Arezoo Hatefi; Frank Drewes; Johanna Björklund; Xuan-Son Vu; Eric Gaussier; Umeå universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Topic Identification; Data Clustering; News Stream Clustering; Semi-Supervised Learning; Unsupervised Learning; Event Topics; News Stories; Multimodal News; Document Classification; Document Clustering; Deep Learning; Deep Clustering; Pre-trained Language Models;

   Sammanfattning : In today's world, following news is crucial for decision-making and staying informed. With the growing volume of daily news, automated processing is essential for timely insights and in aiding individuals and corporations in navigating the complexities of the information society. LÄS MER

3. 2. Image and Data Analysis for Spatially Resolved Transcriptomics : Decrypting fine-scale spatial heterogeneity of tissue's molecular architecture

   Författare :Gabriele Partel; Carolina Wählby; Anna H Klemm; Mats Nilsson; Roland Eils; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; iss; image; processing; clustering; deep learning; GCN; graph;

   Sammanfattning : Our understanding of the biological complexity in multicellular organisms has progressed at tremendous pace in the last century and even more in the last decades with the advent of sequencing technologies that make it possible to interrogate the genome and transcriptome of individual cells. It is now possible to even spatially profile the transcriptomic landscape of tissue architectures to study the molecular organization of tissue heterogeneity at subcellular resolution. LÄS MER

4. 3. Self-supervised deep learning and EEG categorization

   Författare :Mats Svantesson; Magnus Thordstein; Håkan Olausson; Anders Eklund; Gerald Cooray; Linköpings universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; EEG; Deep Learning; Self-supervised; Interrater Agreement; T- SNE;

   Sammanfattning : Deep learning has the potential to be used to improve and streamline EEG analysis. At the present, classifiers and supervised learning dominate the field. Supervised learning depends on target labels which most often are created by human experts manually classifying data. LÄS MER

5. 4. 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

7. 5. 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