Browsing NR vitenarkiv by Author "Jenssen, Robert"
Now showing items 1-20 of 25
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Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
Wickstrøm, Kristoffer; Løkse, Sigurd Eivindson; Kampffmeyer, Michael; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023)Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently to gain insight into, among others, DNNs’ generalization ability. However, it is by no means obvious how to ... -
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy
Wickstrøm, Kristoffer Knutsen; Løkse, Sigurd Eivindson; Kampffmeyer, Michael Christian; Yu, Shujian; Príncipe, José C.; Jenssen, Robert (Journal article; Peer reviewed, 2023) -
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
Wickstrøm, Kristoffer; Østmo, Eirik Agnalt; Radiya, Keyur; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2023-05-09)Deep learning-based approaches for content-based image retrieval (CBIR)of computed tomography (CT) liver images is an active field of research but suffer from some critical limitations. First,they are heavily reliant on ... -
A Contextually Supported Abnormality Detector for Maritime Trajectories
Olesen, Kristoffer Vinther; Boubekki, Ahcene; Kampffmeyer, Michael Christian; Jenssen, Robert; Christensen, Anders Nymark; Hørlück, Sune; Clemmensen, Line H. (Journal article; Peer reviewed, 2023)The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal ... -
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 ... -
Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data
Choi, Changkyu; Kampffmeyer, Michael; Jenssen, Robert; Handegard, Nils Olav; Salberg, Arnt-Børre (Journal article; Peer reviewed, 2023)Multi-frequency echosounder data can provide a broad understanding of the underwater environment in a non-invasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. ... -
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 ... -
Discriminative multimodal learning via conditional priors in generative models
Andrade Mancisidor, Rogelio; Kampffmeyer, Michael Christian; Aas, Kjersti; Jenssen, Robert (Journal article; Peer reviewed, 2023)Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms ... -
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 ... -
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
Trosten, Daniel Johansen; Chakraborty, Rwiddhi; Løkse, Sigurd Eivindson; Wickstrøm, Kristoffer; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where ... -
The Kernelized Taylor Diagram
Wickstrøm, Kristoffer; Johnson, Juan Emmanuel; Løkse, Sigurd Eivindson; Camps-Valls, Gusatu; Mikalsen, Karl Øyvind; Kampffmeyer, Michael; Jenssen, Robert (Journal article; Peer reviewed, 2022)This paper presents the kernelized Taylor diagram, a graphical framework for visualizing similarities between data populations. The kernelized Taylor diagram builds on the widely used Taylor diagram, which is used to ... -
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 ... -
Mixing up contrastive learning: Self-supervised representation learning for time series
Wickstrøm, Kristoffer; Kampffmeyer, Michael; Mikalsen, Karl Øyvind; Jenssen, Robert (Journal article; Peer reviewed, 2022) -
Mixing up contrastive learning: Self-supervised representation learning for time series
Kampffmeyer, Michael; Mikalsen, Karl Øyvind; Jenssen, Robert (Journal article; Peer reviewed, 2022) -
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 ... -
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
Trosten, Daniel Johansen; Løkse, Sigurd Eivindson; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2023)Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially ... -
Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective
Trosten, Daniel Johansen; Jenssen, Robert; Kampffmeyer, Michael (Journal article; Peer reviewed, 2021) -
Selective Imputation for Multivariate Time Series Datasets with Missing Values
Blazquez-Garcia, Ane; Wickstrøm, Kristoffer Knutsen; Yu, Shujian; Mikalsen, Karl Øyvind; Boubekki, Ahcene; Conde, Angel; Mori, Usue; Jenssen, Robert; Lozano, Jose A. (Peer reviewed; Journal article, 2023) -
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 ...