• Deep generative models for reject inference in credit scoring 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)
      Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of ...
    • Dense dilated convolutions merging network for land cover classification 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Peer reviewed, 2020)
      Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the ...
    • Generating customer's credit behavior with deep generative models 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2022)
      Innovation is considered essential for today's organizations to survive and thrive. Researchers have also stressed the importance of leadership as a driver of followers' innovative work behavior (FIB). Yet, despite a large ...
    • Learning latent representations of bank customers with the Variational Autoencoder 

      Andrade Mancisidor, Rogelio; Kampffmeyer, Michael; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2020)
      Learning data representations that reflect the customers’ creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the credit risk assessment in retail banks. In ...
    • Machine Learning + Marine Science: Critical Role of Partnerships in Norway 

      Handegard, Nils Olav; Eikvil, Line; Jenssen, Robert; Kampffmeyer, Michael; Salberg, Arnt Børre; Malde, Ketil (Others, 2021)
      In this essay, we review some recent advances in developing machine learning (ML) methods for marine science applications in Norway. We focus mostly on deep learning (DL) methods and review the challenges we have faced in ...
    • Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Chapter, 2020)
      We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes ...
    • Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective 

      Trosten, Daniel Johansen; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2021)
    • Self-Constructing Graph Convolutional Networks for Semantic Labeling 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt-Børre (Chapter, 2020)
      Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the ...
    • Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images 

      Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre (Journal article; Peer reviewed, 2021)
      Capturing global contextual representations in remote sensing images by exploiting long-range pixel-pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open ...
    • Semi-supervised target classification in multi-frequency echosounder data 

      Choi, Changkyu; Kampffmeyer, Michael; Handegard, Nils Olav; Salberg, Arnt Børre; Brautaset, Olav; Eikvil, Line; Jenssen, Robert (Journal article; Peer reviewed, 2021)
      Acoustic target classification in multi-frequency echosounder data is a major interest for the marine ecosystem and fishery management since it can potentially estimate the abundance or biomass of the species. A key problem ...
    • The 1st Agriculture-Vision Challenge: Methods and Results 

      Chiu, Mang Tik; Xingqiang, Xu; Wang, Kai; Hobbs, Jennifer; Hovakimyan, Naira; Huang, Thomas S.; Shi, Honghui; Wei, Yunchao; Huang, Zilong; Schwing, Alexander; Brunner, Robert; Dozier, Ivan; Dozier, Wyatt; Ghandilyan, Karen; Wilson, David; Park, Hyunseong; Kim, Junhee; Kim, Sungho; Liu, Qinghui; Kampffmeyer, Michael; Jenssen, Robert; Salberg, Arnt Børre; Barbosa, Alexandre; Trevisan, Rodrigo; Zhao, Bingchen; Yu, Shaozuo; Yang, Siwei; Wang, Yin; Sheng, Hao; Chen, Xiao; Su, Jingyi; Rajagopal, Ram; Ng, Andrew; Huynh, Van Thong; Kim, Soo-Hyung; Na, In-Seop; Baid, Ujjwal; Innani, Shubham; Dutande, Prasad; Baheti, Bhakti; Talbar, Sanjay; Tang, Jianyu (Chapter, 2020)
    • Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series 

      Wickstrøm, Kristoffer Knutsen; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Revhaug, Arthur; Jenssen, Robert (Journal article; Peer reviewed, 2020)