Avancerad sökning

Visar resultat 1 - 5 av 27 uppsatser som matchar ovanstående sökkriterier.

1. 1. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Eddie Nevander Hellström; Johan Slettengren; [2023]
   Nyckelord :;

   Sammanfattning : Brain-inspired computing is a promising research field, with potential to encouragebreakthroughs within machine learning and enable us to solve complex problems in a moreefficient way. This study aims to compare the performance of brain-like machine learningalgorithms for time series forecasting. LÄS MER

3. 2. Unauthorised Session Detection with RNN-LSTM Models and Topological Data Analysis

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Nazar Maksymchuk Netterström; [2023]
   Nyckelord :Recurrent Neural Network; Long-Short-Term-Memory; Topological Data Analysis; Session based data; Anomaly detection; Time-series analysis; Imbalanced data; Master thesis; Neurala nätverk; Topologisk data analys; Detektion av avvikelse; Sessionsbaserad data; Tidserieanalys; Inbalancerad data; Masteruppsats;

   Sammanfattning : This thesis explores the possibility of using session-based customers data from Svenska Handelsbanken AB to detect fraudulent sessions. Tools within Topological Data Analysis are employed to analyse customers behavior and examine topological properties such as homology and stable rank at the individual level. LÄS MER

4. 3. Evaluating the Effects of Neural Noise in the Multidigraph Learning Rule

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustav Bressler; Sigvard Dackevall; [2023]
   Nyckelord :;

   Sammanfattning : 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. LÄS MER

5. 4. Safe Reinforcement Learning for Social Human-Robot Interaction : Shielding for Appropriate Backchanneling Behavior

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Mohamed Akif; [2023]
   Nyckelord :Human-Robot Interaction; Backchanneling; Social Robots; Safe Reinforcement Learning; Shielding; Recurrent Neural Network; Gated Recurrent Unit; Människa-Robot Interaktion; Uppbackning; Sociala Robotar; Säker Förstärkningsinlärning; Avskärmning; Återkommande Neurala Nätverk; Gated Återkommande Enhet;

   Sammanfattning : Achieving appropriate and natural backchanneling behavior in social robots remains a challenge in Human-Robot Interaction (HRI). This thesis addresses this issue by utilizing methods from Safe Reinforcement Learning in particular shielding to improve social robot backchanneling behavior. LÄS MER

7. 5. Deep learning, LSTM and Representation Learning in Empirical Asset Pricing

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Benjamin von Essen; [2022]
   Nyckelord :LSTM; empirical asset pricing; deep learning; representation learning; neural networks; LSTM; empirisk tillgångsvärdering; djupinlärning; representationsinlärning; neurala nätverk;

   Sammanfattning : In recent years, machine learning models have gained traction in the field of empirical asset pricing for their risk premium prediction performance. In this thesis, we build upon the work of [1] by first evaluating models similar to their best performing model in a similar fashion, by using the same dataset and measures, and then expanding upon that. LÄS MER