Sökning: "inlärningstekniker"
Visar resultat 1 - 5 av 17 uppsatser innehållade ordet inlärningstekniker.
1. Adversarial robustness of STDP-trained spiking neural networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Adversarial attacks on machine learning models are designed to elicit the wrong behavior from the model. One such attack on image classifiers are maliciously crafted inputs that, to the human eye, look untampered with but have been carefully altered to cause misclassification. LÄS MER
2. A Comparison of CNN and Transformer in Continual Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Within the realm of computer vision tasks, Convolutional Neural Networks (CNN) and Transformers represent two predominant methodologies, often subject to extensive comparative analyses elucidating their respective merits and demerits. This thesis embarks on an exploration of these two models within the framework of continual learning, with a specific focus on their propensities for resisting catastrophic forgetting. LÄS MER
3. Evaluation of the performance of machine learning techniques for email classification
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Manual categorization of a mail inbox can often become time-consuming. Therefore many attempts have been made to use machine learning for this task. One essential Natural Language Processing (NLP) task is text classification, which is a big challenge since an NLP engine is not a native speaker of any human language. LÄS MER
4. Machine learning embedded automation in financial forecasting : A quantitative case study at Ericsson
Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)Sammanfattning : In today’s increasingly data-driven world, time series forecasting is becoming a prevalent practice. Business executives can make better decisions aided by insights from financial forecasts. LÄS MER
5. Unsupervised 3D Human Pose Estimation
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The thesis proposes an unsupervised representation learning method to predict 3D human pose from a 2D skeleton via a VAEGAN (Variational Autoencoder Generative Adversarial Network) hybrid network. The method learns to lift poses from 2D to 3D using selfsupervision and adversarial learning techniques. LÄS MER