Search for dissertations about: "new neural networks"

Showing result 1 - 5 of 154 swedish dissertations containing the words new neural networks.

2. 1. Characterizing Piecewise Linear Neural Networks

   Author : Anton Johansson; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; automotive applications.; neural network; machine learning; piecewise linear;

   Abstract : Neural networks utilizing piecewise linear transformations between layers have in many regards become the default network type to use across a wide range of applications. Their superior training dynamics and generalization performance irrespective of the nature of the problem has resulted in these networks achieving state of the art results on a diverse set of tasks. READ MORE

4. 2. Word Vector Representations using Shallow Neural Networks

   Author : Oluwatosin Adewumi; Marcus Liwicki; Marco Kuhlmann; Luleå tekniska universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; Word vectors; NLP; Neural networks; Embeddings; Maskininlärning; Machine Learning;

   Abstract : This work highlights some important factors for consideration when developing word vector representations and data-driven conversational systems. The neural network methods for creating word embeddings have gained more prominence than their older, count-based counterparts. READ MORE

5. 3. Infrared Neural Modulation: Photothermal Effects on Cortex Neurons Using Infrared Laser Heating

   Author : Qingling Xia; Tobias Nyberg; Björn Granseth; KTH; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; neural modulation; infrared laser; in - vitro experiment; multi - electrode arrays MEA ; heating model; temperature; neural networks; infrared neural inhibition INI ; hyperexcitation;

   Abstract : It would be of great value to have a precise and non-damaging neuromodulation technique in the field of basic neuroscience research and for clinical treatment of neurological diseases. Infrared neural modulation (INM) is a new modulation modality developed in the last decade, which uses pulsed or continues infrared (IR) light with a wavelength of 1200 to 2200 nm to directly alter neural signals. READ MORE

6. 4. Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer's Disease, Brain Tumors, to Assisted Living

   Author : Chenjie Ge; Chalmers tekniska högskola; []
   Keywords : MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; MEDICIN OCH HÄLSOVETENSKAP; MEDICAL AND HEALTH SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; convolutional neural networks; Alzheimer s disease detection; machine learning; deep learning; fall detection; glioma subtype classification; generative adversarial networks; recurrent convolutional networks; spiking neural networks; visual prosthesis; semi-supervised learning;

   Abstract : Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer's disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. READ MORE

7. 5. On Improving Validity of Deep Neural Networks in Safety Critical Applications

   Author : Jens Henriksson; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; out-of-distribution; outlier detection; deep neural networks; Safety critical applications;

   Abstract : Context: Deep learning has proven to be a valuable component in object detection and classification, as the technique has shown an increased performance throughput compared to traditional software algorithms. Deep learning refers to the process, in which an optimisation process learns an algorithm through a set of labeled data, where the researcher defines an architecture rather than the algorithm itself. READ MORE