https://dasya.itu.dk/tags/deep-learning/ Recent content in Deep Learning on DASYA Hugo -- gohugo.io en-us © DASYA, IT University of Copenhagen Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-attribution/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-attribution/ Deep convolutional networks are able to learn representation of images, scoring well in tasks such as image classification and object detection. During model training, these networks have the ability to process different input sizes without requiring changes to their architecture. In this project, we would like to investigate the effects that changing input sizes has on these kinds of models. We want to do this by investigating and using data attribution methods, as these techniques can help explain the effect of input data on the predictions of the model. https://dasya.itu.dk/for-students/proposals/archive/pito-data-selection/ Tue, 15 Jul 2025 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/pito-data-selection/ Today’s foundation models are trained on vast amounts of data. The quality and size of this data has a huge impact on the accuracy of these models. Selecting the right amount and variety of data for a given task, however, is a resource-intensive process. In this project, which is part of a larger collaboration, we would like to expand our investigation of state-of-the-art data selection mechanisms from a hardware requirements and resource-efficiency perspective in addition to their impact on model accuracy. https://dasya.itu.dk/for-students/proposals/archive/ajs-medical-vqa/ Thu, 11 May 2023 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/ajs-medical-vqa/ A medical Visual Question Answering (VQA) system can provide meaningful references for both doctors and patients during the treatment process. Different from normal images, a learning setting with medical images is more challenging due limited amounts of data, class-imbalance and the presence of label noise for diagnosis tasks. Moreover, little attention is paid to how the images and meta-data is obtained for medical datasets. Recent studies show that even with high performance on the existing data, algorithms can learn “shortcuts” like visibility of medical tools and fail to generalize. https://dasya.itu.dk/for-students/proposals/archive/dj-pretraining_strategies/ Mon, 30 May 2022 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/dj-pretraining_strategies/ Deep neural networks have been revolutionary in computer vision and publicly available image datasets played an important role in this success. Due to their size, neural networks require vast amounts of data for training. Yet when it comes to medical settings dataset sizes are very limited due to the cost of data annotation, privacy concerns, differences in imaging techniques, and others. In such cases, transfer learning is often used. A most common approach is to use a supervised classifier trained on ImageNet (or any other available large dataset) and finetune its parameters using the target dataset such as clinical images. https://dasya.itu.dk/for-students/proposals/archive/wind-turbine-electricity-production-prediction/ Mon, 16 Dec 2019 00:00:00 +0000 https://dasya.itu.dk/for-students/proposals/archive/wind-turbine-electricity-production-prediction/ The goal of the project is to explore the accuracy of electricity production predictions based on historical data and weather predictions. This may be tackled as a sequence prediction problem using recurrent neural networks The long term goal is to incorporate wind turbines in the reserve market for electricity. https://dasya.itu.dk/for-students/projects/archive/speechrecognition/ Sun, 15 Dec 2019 00:00:00 +0000 https://dasya.itu.dk/for-students/projects/archive/speechrecognition/ Investigating the behavior of acoustic model training via deep learning on co-processors of different price groups https://dasya.itu.dk/for-students/projects/archive/speechrecognitionpre/ Sat, 01 Jun 2019 00:00:00 +0000 https://dasya.itu.dk/for-students/projects/archive/speechrecognitionpre/ How do we implement and efficiently train a centralized deep learning system converting speech to text in a scalable manner?