Sort by
Refine Your Search
-
Modern map-based systems and location-based services rely heavily on the ability to efficiently provide navigation services and the capability to search points of interests (POIs) based on their location or textual information. The aim of this project is to build a next-generation navigation...
-
as child exploitation material). It will involve the use of deep neural networks for image, video, audio, social network, and/or text classification. The Faculty of Information Technology has a mission
-
networks so that they can accurately identify anomalies in the presence of concept drift. We would like to consider different types of changes in graph structures, such as emergence/deletion of new nodes
-
with a variety of Deep Learning frameworks, such as U-Net, ResNet, etc. One of the main challenges in medical image segmentation and classification is that the results must be explainable; hence
-
development process for DL, covering requirement analysis, data collection and labeling, data cleaning, network design, training, testing, and operation. Required knowledge deep learning, natural
-
models (eg auto-encoders and generative adversarial networks) and reinforcement/imitation learning algorithms for Markov Decision Processes. The application areas are different problems in text processing
-
. Wallace ", Computer Journal , Vol. 51, No. 5 (Sept. 2008) [Christopher Stewart WALLACE (1933-2004) memorial special issue [and front cover and back cover ]], pp 523-560 (and here ). www.doi .org
-
methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace ", Computer Journal , Vol. 51, No. 5 (Sept. 2008
-
Ensembles over Continuous Data", PETS 2022 - "Privacy-Preserving Video Classification with Convolutional Neural Networks", ICML 2021 - "Privacy-Preserving Feature Selection with Secure Multiparty Computation
-
to detect unknown signals from gravitational-wave observatories across planet Earth. References Comley, Joshua W. and D.L. Dowe (2003). General Bayesian Networks and Asymmetric Languages, Proc. 2nd Hawaii