Nettsider med emneord «machine learning»

Signal processing, image analysis, and machine learning for applications in medical imaging, sonar, seismics, and remote sensing.

Obstructive sleep apnea (OSA) is a common but severely under-diagnosed sleep disorder that affects the natural breathing cycle during sleep with the periods of reduced respiration or no airflow at all. It is our long-term goal to increase the percentage of diagnosed OSA cases, reduce the time to diagnosis, and support long term monitoring of patients with user friendly and cost-efficient tools for sleep analysis at home. Core elements are mobile computing platforms (e.g., smartphones), consumer electronics sensors, and machine learning for OSA detection.

Cardiac related disease is the number one cause of death in the Western world, including Norway. Echocardiography is the most important imaging tool for the cardiologist to assess cardiac function. An echo examination of the heart is real time, cost effective and can be performed without discomfort to the patient and without harmful radiation. These are great advantages compared to other medical imaging modalities.

The SANT project develops resources for Sentiment Analysis for Norwegian Text. While coordinated by the Language Technology Group (LTG) at IFI/UiO, collaborating partners include NRK, Schibsted and Aller Media.

The SANT project will be presented at the Cutting Edge Festival.

Two new papers from SANT were presented at the NoDaLiDa conference.

In joint work, SANT and NLPL/EOSC-Nordic have trained and released the first large-scale transformer-based language model for Norwegian: NorBERT!

In this ongoing cross-disciplinary collaboration, researchers in Language Technology (LT) and Political Science (PS) are applying supervised and unsupervised machine learning methods to data from the Norwegian parliament in order to gather knowledge spanning across different dimensions.

Classification models are often used to make decisions that affect humans: whether to approve a loan application, extend a job offer, or provide insurance. In such applications, it is desirable for the individuals to not only knowing the results but also have the ability to change the decisions of the model. For example, when a person is denied a loan by a credit scoring model, in addition to know why he/she can not received the loan, it is meaningful for the person to know what he/she can do to influence the decision, i.e. what are the input variables that, if values are changed, can alter the decision of the model. Otherwise, without this information, he/she will be denied the loan as long as the model is deployed, and – more importantly – will lack agency over a decision that affects their livelihood.

Ontologies are developed for representing requirements and specifications that can help domain experts and engineers do various tasks in the real world. Usually, as the ontologies are quite large, it takes a long time for human users to understand the ontologies and it is also quite difficult for machines to do reasoning on such large ontologies. The goal is that we can explore different machine-learning techniques to extract relevant parts of the ontology and reduce reasoning time.