Browsing Faculty of Science by Subject "Deep learning"
Now showing items 1-13 of 13
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Comparative analysis of deep learning and graph cut algorithms for cell image segmentation
(2020-08-01)Image segmentation is a commonly used technique in digital image processing with many applications in the area of computer vision and medical image analysis. The goal of image segmentation is to partition an image into ... -
Deep learning approach to discontinuity-preserving image registration
(2020-05-01)Image registration is an indispensable tool in medical image analysis. Traditionally, registration algorithms are aimed at aligning image pairs using regularizers to impose smoothness restrictions on unknown deformation ... -
Design and evaluation of a novel convolutional neural network for short-term vehicle multi-traffic prediction
(2019-08-01)Short-term vehicle traffic forecasting is about predicting how traffic indicators are going to be in the near future. The main traffic parameters are: traffic volume, traffic speed, and congestion state. In this thesis, ... -
DL-based defense against polymorphic network attacks
(2024-01-01)Network security is of vital importance in our world dominated by internet systems. These systems are vulnerable to large-scale rapidly evolving attacks by sophisticated cyber attackers who can have an upper edge over the ... -
Enhanced knowledge distillation by auxiliary classifiers
(2021-08-01)Deep neural models have shown promising results in various areas, e.g., computer vision and natural language processing, at the cost of high computation and storage resource consumption. These characteristics of deep neural ... -
Group representation learning for group recommendation
(2021-01-01)Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Existing group recommendation methods mostly learn group members' individual ... -
An investigation into the use of ConvNext within IICS/IIDS framework for person Re-ID
(2023-05-01)In this thesis, we explore the integration of ConvNeXt, a CNN-based network inspired by vision transformers, into the Intra and Inter Camera Similarity (IICS) and Intra and Inter Domain Similarity (IIDS) frameworks for ... -
JoVA-hinge: joint variational autoencoders for personalized recommendation with implicit feedback
(2020-12-01)Recently, Variational Autoencoders (VAEs) have shown remarkable performance in collaborative filtering (CF) with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict ... -
Molecular dynamics simulations and neural network solutions for applications in biophysics
(2022-12-01)As computing resources evolved and became more accessible over time, much of scientific research shifted towards utilizing computational techniques. In particular, biophysics is a field of science that has continually ... -
On deep learning in physics
(2021-04-01)Machine learning, and most notably deep neural networks, have seen unprecedented success in recent years due to their ability to learn complex nonlinear mappings by ingesting large amounts of data through the process of ... -
Opportunities for the deep neural network method of solving partial differential equations in the computational study of biomolecules driven through periodic geometries
(2022-08-01)As deep learning emerged in the 2010s to become a groundbreaking technology in machine vision and natural language processing, it also ushered in many new algorithms for use in scientific research. Among these is the neural ... -
Preferential proximal policy optimization in reinforcement learning
(2023-12-01)The Proximal Policy Optimization (PPO), a policy gradient method, excels in reinforcement learning with its ”surrogate” objective function and stochastic gradient ascent. However, PPO does not fully consider the significance ... -
Using machine learning methods to aid scientists in laboratory environments
(2019-12-01)As machine learning gains popularity as a scientific instrument, we look to create methods to implement it as a laboratory tool for researchers. In the first of two projects, we discuss creating a real-time interference ...