Nettsider med emneord «Machine Learning» - Side 3

Oppgaven utføres i samarbeid med Forsvarets forskningsinstitutt (FFI) på Kjeller. 

An important priority for LTG in recent years has been to create NLP resources for the Norwegian language, both in terms of modeling and datasets. This page provides an overview of our existing and ongoing projects to support Norwegian NLP.

In the field of robotics, creating grippers that can perform tasks similar to the capabilities of a human hand, is still a challenge yet to be solved. 

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

The Tactile Internet –a communication network that is capable of delivering real-time control, touch, and sensing/actuation information through sufficiently reliable, responsive, and intelligent connectivity – is revolutionizing the understanding of what is possible through wireless communication systems, pushing boundaries of Internet-based applications to remote physical interaction. Such remote interaction capability can be used in surgery, driving, drone-based transportation, immersive education, and adventure, etc. Although the community envisions the bright future of the tactile internet, few works implement the physical platform and specify the blockages in enabling a smooth quality of experience (QoE) during the interaction. In this project, the candidate(s) will work in a team to identify those blockages and propose the novel algorithm in network stack to enable the avatar in the real world.

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.

The SANT project aims to create training data and machine-learned models for Sentiment Analysis for Norwegian Text. While coordinated by the Language Technology Group at IFI/UiO, collaborating partners include NRK, Schibsted and Aller Media.

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.

How is rhythm processed in the human brain, and how can we model rhythm in machines? These are central research questions at the new RITMO Centre of Excellence.

We aim to take inspiration from rhythm in humans and other biological systems and develop models of rhythmic motion which can be applied to robotic and computing systems.