Advanced search

Showing result 1 - 5 of 17 essays matching the above criteria.

1. 1. Evaluating the Effects of Neural Noise in the Multidigraph Learning Rule

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

   Author : Gustav Bressler; Sigvard Dackevall; [2023]
   Keywords : ;

   Abstract : There exists a knowledge gap in the field of Computational Neuroscience, where many learning models for neural networks fail to take into account the influence of neural noise. The purpose of this thesis was to address this knowledge gap by investigating the robustness of the Multidigraph learning rule (MDGL) when exposed to two kinds of neural noise: external noise and internal noise. READ MORE

3. 2. Multi-Agent Information Gathering Using Stackelberg Games

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

   Author : Yiming Hu; [2023]
   Keywords : Information gathering; Autonomous exploration; Multi-agent coordination; Multi-agent system; Informationsinsamling; Autonom utforskning; Samordning av flera agenter; multiagentsystem;

   Abstract : Multi-agent information gathering (MA-IG) enables autonomous robots to cooperatively collect information in an unfamiliar area. In some scenarios, the focus is on gathering the true mapping of a physical quantity such as temperature or magnetic field. READ MORE

4. 3. Machine learning in predictive maintenance of industrial robots

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

   Author : Simone Morettini; [2021]
   Keywords : Predictive maintenance; Industrial robots; Gaussian process regression; Exponentron; Hybrid algorithms; Time series prediction.; Prediktivt underhåll; Industriella robotar; Gaussian process regression; Exponentron; Hybridalgoritmer; Prediktivt av tidsserier.;

   Abstract : Industrial robots are a key component for several industrial applications. Like all mechanical tools, they do not last forever. The solution to extend the life of the machine is to perform maintenance on the degraded components. READ MORE

5. 4. Incorporating Metadata Into the Active Learning Cycle for 2D Object Detection

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

   Author : Karsten Stadler; [2021]
   Keywords : Active learning; Deep Learning; Object detection; Metadata; Nuscenes Nuimages; Gaussian mixture model; Rejection sampling; Monte-Carlo methods; Aktiv Inlärning; Djupinlärning; Objektdetektering; metadata; Nuscenes Nuimages; Gaussisk blandingsmodell; Rejection sampling; Monte-Carlo metoder;

   Abstract : In the past years, Deep Convolutional Neural Networks have proven to be very useful for 2D Object Detection in many applications. These types of networks require large amounts of labeled data, which can be increasingly costly for companies deploying these detectors in practice if the data quality is lacking. READ MORE

6. 5. Particle Filter Bridge Interpolation in GANs

   University essay from KTH/Matematisk statistik

   Author : Viktor Käll; Erik Piscator; [2021]
   Keywords : Generative modeling; Generative adversarial network; Convolutional neural network; Stochastic interpolation; Gaussian process; Gaussian bridge process; Sequential Monte Carlo; Particle filter; Generativ modellering; Generative adversarial network; Neuralt faltningsnätverk; Stokastisk interpolation; Gaussisk process; Gaussisk bryggprocess; Sekventiell Monte Carlo; Partikelfilter;

   Abstract : Generative adversarial networks (GANs), a type of generative modeling framework, has received much attention in the past few years since they were discovered for their capacity to recover complex high-dimensional data distributions. These provide a compressed representation of the data where all but the essential features of a sample is extracted, subsequently inducing a similarity measure on the space of data. READ MORE