Essays about: "spatial learning and memory"

Showing result 1 - 5 of 23 essays containing the words spatial learning and memory.

1. 1. Deep Learning-based Regularizers for Cone Beam Computed Tomography Reconstruction

   University essay from KTH/Matematisk statistik

   Author : Sabina Syed; Josefin Stenberg; [2023]
   Keywords : Adversarial Convex Regularization; Computer Vision; Cone Beam Computed Tomography; Convolutional Neural Networks; Deep Learning; Image Reconstruction; Adversarial Convex Regularization; Bildrekonstruktion; Datorseende; Djupinlärning; Faltningsnätverk; Volymtomografi;

   Abstract : 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. READ MORE

3. 2. Confidential Federated Learning with Homomorphic Encryption

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Zekun Wang; [2023]
   Keywords : Cloud Technology; Confidential Computing; Federated Learning; Homomorphic Encryption; Trusted Execution Environment; Molnteknik; Konfidentiell databehandling; Federerad inlärning; Homomorfisk kryptering; Betrodd körningsmiljö;

   Abstract : Federated Learning (FL), one variant of Machine Learning (ML) technology, has emerged as a prevalent method for multiple parties to collaboratively train ML models in a distributed manner with the help of a central server normally supplied by a Cloud Service Provider (CSP). Nevertheless, many existing vulnerabilities pose a threat to the advantages of FL and cause potential risks to data security and privacy, such as data leakage, misuse of the central server, or the threat of eavesdroppers illicitly seeking sensitive information. READ MORE

4. 3. Understanding the Determinants of Car Ownership : A Regression and Neural Network Study

   University essay from Uppsala universitet/Avdelningen för beräkningsvetenskap

   Author : Olle Kindvall; Vegard Pettersson; [2023]
   Keywords : Machine learning; panel data; panel data regression; LSTM; parking norm; car ownership;

   Abstract : This thesis aims to understand the determinants of car ownership in the Swedish regions containing the largest cities: Skåne, Stockholm, and Västra Götaland. This is done by performing a fixed effects regression analysis as well as creating and comparing different predictive models. Both socioeconomic and spatial factors are looked at. READ MORE

5. 4. Regularizing Vision-Transformers Using Gumbel-Softmax Distributions on Echocardiography Data

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Alfred Nilsson; [2023]
   Keywords : Deep Learning; Vision-Transformers; Echocardiography; Feature Selection; Gumbel-Softmax; Concrete Autoencoders; Regression; Djupinlärning; Vision-Transformers; Ekokardiografi; Feature Selection; GumbelSoftmax; Concrete Autoencoders; Regression;

   Abstract : This thesis introduces an novel approach to model regularization in Vision Transformers (ViTs), a category of deep learning models. It employs stochastic embedded feature selection within the context of echocardiography video analysis, specifically focusing on the EchoNet-Dynamic dataset. READ MORE

6. 5. Pilot Study on Working Memory : Investigating Single Trial Decoding to Find the Best Stimulus and Target for a Future Personalized Neurofeedback

   University essay from KTH/Medicinteknik och hälsosystem

   Author : Erik Gasparini; [2023]
   Keywords : Visual working memory; Personalised neurofeedback; Visual stimulus; Targeted brain region.;

   Abstract : A standard Neurofeedback approach to mitigate the working memory decline in some fragile groups (elderly, subjects affected by stroke or Alzheimer's disease) can be suboptimal for some patients. The goal of this research is to investigate which visual stimulus (among colour, geometrical shape, direction, and symbol) is the most suited for each of the six healthy participants and which brain areas are the most discriminative, during the maintenance of a presented stimulus in a retro-cue-based working memory experiment. READ MORE