Robert invited speaker

Robert was an invited speaker Sept 7 2017 at the Kongsberg Technology Conference at Sundvolden Hotel.

Michael at CMU!

Michael Kampffmeyer visits Carnegie Mellon University (CMU) for 10 months starting Sept 2017! Michael is  an expert on deep learning (DL), and has published works on development of convolutional neural networks in remote sensing, on DL clustering, and on so-called kernelized auto encoders. We wish Michael good luck in the US!

Robert opponent at UiS

Ali B. Rad successfully defended his PhD thesis at the University of Stavanger (UiS) 31st Aug’17. Opponents were Pablo Laguna, University of Zaragoza, and Robert Jenssen, UiT.

Hiring new postdoc

Candidates for our new postdoc position is now under review. We have several good candidates who has applied for the position!

Cristina Soguero-Ruiz visiting!

Cristina Soguero-Ruiz from the Rey Juan Carlos University in Spain is once again visiting the UiT Machine Learning Group 23. Aug – 30. Aug. 2017. Welcome back!

Andreas working as intern on RNNs!

Andreas spent the summer with the UiT Machine Learning Group working on Recurrent Neural Networks for classification of time series with missing data in a health analytics and electronic patient journal context.

New intern! Kristoffer

Kristoffer Wickstrøm has joined UiT Machine Learning Group as a summer intern. Kristoffer is working on polyp segmentation using fully convolutional neural networks. Areas of interest include machine learning and deep learning, with a special interest in convolutional neural networks. His supervisors are Michael Kampffmeyer, Karl Øyvind Mikalsen, and Robert Jenssen.

SCIA 2017 went well!

UiT Machine Learning Group successfully organised SCIA 2017 in June in Tromsø. There were around 150 participants from all over the world. Please see   Or Facebook

Michael won best paper award!

Michael Kampffmeyer, UiT Machine Learning Group, won the best student paper award at SCIA 2017 for his paper “Deep Kernelized Autoencoders”. Congratulations!

New paper on Arxiv!

Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruiz, Robert Jenssen (Submitted on 3 Apr 2017) Similarity-based approaches represent a promising direction for time series analysis. However, many such methods rely on parameter tuning and have shortcomings if the time series are multivariate (MTS) Read More