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2020 – today
- 2024
[j57]Duy Khoi Tran
, Van Nhan Nguyen, Davide Roverso, Robert Jenssen, Michael Kampffmeyer:
LSNetv2: Improving weakly supervised power line detection with bipartite matching. Expert Syst. Appl. 250: 123773 (2024)- [j56]Harald L. Joakimsen, Iver Martinsen, Luigi Tommaso Luppino, Andrew McDonald, Scott Hosking, Robert Jenssen:
Interrogating Sea Ice Predictability With Gradients. IEEE Geosci. Remote. Sens. Lett. 21: 1-5 (2024) - [j55]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Discriminative multimodal learning via conditional priors in generative models. Neural Networks 169: 417-430 (2024) - [j54]Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer:
Leveraging tensor kernels to reduce objective function mismatch in deep clustering. Pattern Recognit. 149: 110229 (2024) - [j53]Luigi Tommaso Luppino, Mads A. Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen, Stian Normann Anfinsen:
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. IEEE Trans. Neural Networks Learn. Syst. 35(1): 60-72 (2024) - [c66]Changkyu Choi, Shujian Yu, Michael Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Robert Jenssen:
DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning. ICASSP 2024: 7170-7174 - [c65]Robert Jenssen:
MAP IT to Visualize Representations. ICLR 2024 - [c64]Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
Cauchy-Schwarz Divergence Information Bottleneck for Regression. ICLR 2024 - [c63]Bjørn Leth Møller, Christian Igel, Kristoffer Knutsen Wickstrøm, Jon Sporring, Robert Jenssen, Bulat Ibragimov:
Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks. ICML 2024 - [c62]Jørgen Aarmo Lund, Karl Øyvind Mikalsen, Per Joel Burman, Ashenafi Zebene Woldaregay, Robert Jenssen:
Instruction-guided deidentification with synthetic test cases for Norwegian clinical text. NLDL 2024: 145-152 - [i62]Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
Cauchy-Schwarz Divergence Information Bottleneck for Regression. CoRR abs/2404.17951 (2024) - [i61]Mingfei Lu, Shujian Yu, Robert Jenssen, Badong Chen:
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications. CoRR abs/2405.04061 (2024) - [i60]Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen:
Explaining time series models using frequency masking. CoRR abs/2406.13584 (2024) - 2023
- [j52]Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Comput. Medical Imaging Graph. 107: 102239 (2023) - [j51]Kristoffer K. Wickstrøm, Sigurd Løkse, Michael C. Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Analysis of Deep Convolutional Neural Networks Using Tensor Kernels and Matrix-Based Entropy. Entropy 25(6): 899 (2023) - [j50]Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Ahcène Boubekki, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen:
RELAX: Representation Learning Explainability. Int. J. Comput. Vis. 131(6): 1584-1610 (2023) - [j49]Stine Hansen, Srishti Gautam, Suaiba Amina Salahuddin, Michael Kampffmeyer, Robert Jenssen:
ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. Medical Image Anal. 89: 102870 (2023) - [j48]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
This looks More Like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. Pattern Recognit. 136: 109172 (2023) - [j47]Ane Blázquez-García, Kristoffer Wickstrøm, Shujian Yu, Karl Øyvind Mikalsen, Ahcène Boubekki, Angel Conde, Usue Mori, Robert Jenssen, José Antonio Lozano:
Selective Imputation for Multivariate Time Series Datasets With Missing Values. IEEE Trans. Knowl. Data Eng. 35(9): 9490-9501 (2023) - [c61]Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer:
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings. CVPR 2023: 7527-7536 - [c60]Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael C. Kampffmeyer:
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering. CVPR 2023: 23976-23985 - [c59]Durgesh Singh, Ahcène Boubekki, Robert Jenssen, Michael C. Kampffmeyer:
Supercm: Revisiting Clustering for Semi-Supervised Learning. ICASSP 2023: 1-5 - [c58]Eirik Agnalt Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Robert Jenssen:
View it Like a Radiologist: Shifted Windows for Deep Learning Augmentation Of CT Images. MLSP 2023: 1-6 - [i59]Shujian Yu, Hongming Li, Sigurd Løkse, Robert Jenssen, José C. Príncipe:
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making. CoRR abs/2301.08970 (2023) - [i58]Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer:
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings. CoRR abs/2303.09352 (2023) - [i57]Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael C. Kampffmeyer:
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering. CoRR abs/2303.09877 (2023) - [i56]Eirik Agnalt Østmo, Kristoffer K. Wickstrøm, Keyur Radiya, Michael C. Kampffmeyer, Robert Jenssen:
View it like a radiologist: Shifted windows for deep learning augmentation of CT images. CoRR abs/2311.14990 (2023) - 2022
- [j46]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Generating customer's credit behavior with deep generative models. Knowl. Based Syst. 245: 108568 (2022) - [j45]Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer:
Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Medical Image Anal. 78: 102385 (2022) - [j44]Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen:
Mixing up contrastive learning: Self-supervised representation learning for time series. Pattern Recognit. Lett. 155: 54-61 (2022) - [j43]Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Sebastiano Bruno Serpico, Robert Jenssen, Stian Normann Anfinsen:
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. IEEE Trans. Geosci. Remote. Sens. 60: 1-22 (2022) - [j42]Ahcène Boubekki, Jonas Nordhaug Myhre, Luigi Tommaso Luppino, Karl Øyvind Mikalsen, Arthur Revhaug, Robert Jenssen:
Clinically Relevant Features for Predicting the Severity of Surgical Site Infections. IEEE J. Biomed. Health Informatics 26(4): 1794-1801 (2022) - [c57]Suaiba Amina Salahuddin, Stine Hansen, Srishti Gautam, Michael Kampffmeyer, Robert Jenssen:
A self-guided anomaly detection-inspired few-shot segmentation network. CVCS 2022 - [c56]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation. ISBI 2022: 1-5 - [c55]Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
The Kernelized Taylor Diagram. NAIS 2022: 125-131 - [c54]Srishti Gautam, Ahcène Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer:
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. NeurIPS 2022 - [c53]Huamin Ren, Xiaomeng Su, Robert Jenssen, Jingyue Li, Stian Normann Anfinsen:
Attention-guided Temporal Convolutional Network for Non-intrusive Load Monitoring. SmartGridComm 2022: 419-425 - [c52]Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, José C. Príncipe:
Principle of relevant information for graph sparsification. UAI 2022: 2331-2341 - [i55]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation. CoRR abs/2201.03559 (2022) - [i54]Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer:
Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels. CoRR abs/2203.02048 (2022) - [i53]Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen:
Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series. CoRR abs/2203.09270 (2022) - [i52]Kaizhong Zheng, Shujian Yu, Baojuan Li, Robert Jenssen, Badong Chen:
BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck. CoRR abs/2205.03612 (2022) - [i51]Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Løkse, Gustau Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
The Kernelized Taylor Diagram. CoRR abs/2205.08864 (2022) - [i50]Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, José C. Príncipe:
Principle of Relevant Information for Graph Sparsification. CoRR abs/2206.00118 (2022) - [i49]Kristoffer Knutsen Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Christian Kampffmeyer, Robert Jenssen:
A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. CoRR abs/2207.04812 (2022) - [i48]Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina M.-C. Höhne, Michael Kampffmeyer:
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model. CoRR abs/2210.08151 (2022) - 2021
- [j41]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Learning latent representations of bank customers with the Variational Autoencoder. Expert Syst. Appl. 164: 114020 (2021) - [j40]Stine Hansen, Samuel Kuttner, Michael Kampffmeyer, Tom-Vegard Markussen, Rune Sundset, Silje Kjærnes Øen, Live Eikenes, Robert Jenssen:
Unsupervised supervoxel-based lung tumor segmentation across patient scans in hybrid PET/MRI. Expert Syst. Appl. 167: 114244 (2021) - [j39]Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint optimization of an autoencoder for clustering and embedding. Mach. Learn. 110(7): 1901-1937 (2021) - [j38]Van Nhan Nguyen, Robert Jenssen, Davide Roverso:
LS-Net: fast single-shot line-segment detector. Mach. Vis. Appl. 32(1): 12 (2021) - [j37]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. Pattern Recognit. 115: 107896 (2021) - [j36]Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. IEEE J. Biomed. Health Informatics 25(7): 2435-2444 (2021) - [j35]Shujian Yu, Kristoffer Wickstrøm, Robert Jenssen, José C. Príncipe:
Understanding Convolutional Neural Networks With Information Theory: An Initial Exploration. IEEE Trans. Neural Networks Learn. Syst. 32(1): 435-442 (2021) - [j34]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series. IEEE Trans. Neural Networks Learn. Syst. 32(5): 2169-2179 (2021) - [c51]Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, José C. Príncipe:
Measuring Dependence with Matrix-based Entropy Functional. AAAI 2021: 10781-10789 - [c50]Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer:
Reconsidering Representation Alignment for Multi-View Clustering. CVPR 2021: 1255-1265 - [c49]Huamin Ren, Filippo Maria Bianchi, Jingyue Li, Rasmus L. Olsen, Robert Jenssen, Stian Normann Anfinsen:
Towards Applicability: A Comparative Study on Non-Intrusive Load Monitoring Algorithms. ICCE 2021: 1-5 - [c48]Daniel J. Trosten, Robert Jenssen, Michael C. Kampffmeyer:
Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective. NLDL 2021 - [i47]Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, José C. Príncipe:
Measuring Dependence with Matrix-based Entropy Functional. CoRR abs/2101.10160 (2021) - [i46]Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer:
Reconsidering Representation Alignment for Multi-view Clustering. CoRR abs/2103.07738 (2021) - [i45]Óscar Escudero-Arnanz, Joaquín Álvarez-Rodríguez, Karl Øyvind Mikalsen, Robert Jenssen, Cristina Soguero-Ruíz:
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit. CoRR abs/2107.10398 (2021) - [i44]Srishti Gautam, Marina M.-C. Höhne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer:
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation. CoRR abs/2108.12204 (2021) - [i43]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Discriminative Multimodal Learning via Conditional Priors in Generative Models. CoRR abs/2110.04616 (2021) - [i42]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks. CoRR abs/2111.03845 (2021) - [i41]Kristoffer K. Wickstrøm, Daniel J. Trosten, Sigurd Løkse, Karl Øyvind Mikalsen, Michael C. Kampffmeyer, Robert Jenssen:
RELAX: Representation Learning Explainability. CoRR abs/2112.10161 (2021) - 2020
- [j33]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Deep generative models for reject inference in credit scoring. Knowl. Based Syst. 196: 105758 (2020) - [j32]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps. Medical Image Anal. 60 (2020) - [j31]Shujian Yu, Luis Gonzalo Sánchez Giraldo, Robert Jenssen, José C. Príncipe:
Multivariate Extension of Matrix-Based Rényi's $\alpha$α-Order Entropy Functional. IEEE Trans. Pattern Anal. Mach. Intell. 42(11): 2960-2966 (2020) - [j30]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Dense Dilated Convolutions' Merging Network for Land Cover Classification. IEEE Trans. Geosci. Remote. Sens. 58(9): 6309-6320 (2020) - [c47]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation. CVPR Workshops 2020: 199-205 - [c46]Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer A. Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Jun Hee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, Alexandre Barbosa, Rodrigo G. Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng, Van Thong Huynh, Soo-Hyung Kim, In Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay N. Talbar, Jianyu Tang:
The 1st Agriculture-Vision Challenge: Methods and Results. CVPR Workshops 2020: 212-218 - [c45]Jonas Nordhaug Myhre, Matineh Shaker, Mustafa Devrim Kaba, Robert Jenssen, Deniz Erdogmus:
A generic unfolding algorithm for manifolds estimated by local linear approximations. CVPR Workshops 2020: 3735-3743 - [c44]Van Nhan Nguyen, Sigurd Løkse, Kristoffer Wickstrøm, Michael Kampffmeyer, Davide Roverso, Robert Jenssen:
SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks. ECCV (23) 2020: 118-134 - [c43]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Self-Constructing Graph Convolutional Networks for Semantic Labeling. IGARSS 2020: 1801-1804 - [i40]Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Sebastiano Bruno Serpico, Robert Jenssen, Stian Normann Anfinsen:
Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. CoRR abs/2001.04271 (2020) - [i39]Daniel J. Trosten, Michael C. Kampffmeyer, Robert Jenssen:
Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives. CoRR abs/2001.07026 (2020) - [i38]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Robert Jenssen:
A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs. CoRR abs/2002.12359 (2020) - [i37]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Dense Dilated Convolutions Merging Network for Land Cover Classification. CoRR abs/2003.04027 (2020) - [i36]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Self-Constructing Graph Convolutional Networks for Semantic Labeling. CoRR abs/2003.06932 (2020) - [i35]Luigi Tommaso Luppino, Mads A. Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen, Stian Normann Anfinsen:
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. CoRR abs/2004.07011 (2020) - [i34]Mang Tik Chiu, Xingqian Xu, Kai Wang, Jennifer A. Hobbs, Naira Hovakimyan, Thomas S. Huang, Honghui Shi, Yunchao Wei, Zilong Huang, Alexander G. Schwing, Robert Brunner, Ivan Dozier, Wyatt Dozier, Karen Ghandilyan, David Wilson, Hyunseong Park, Jun Hee Kim, Sungho Kim, Qinghui Liu, Michael C. Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg, Alexandre Barbosa, Rodrigo G. Trevisan, Bingchen Zhao, Shaozuo Yu, Siwei Yang, Yin Wang, Hao Sheng, Xiao Chen, Jingyi Su, Ram Rajagopal, Andrew Y. Ng, Van Thong Huynh, Soo-Hyung Kim, In Seop Na, Ujjwal Baid, Shubham Innani, Prasad Dutande, Bhakti Baheti, Sanjay N. Talbar, Jianyu Tang:
The 1st Agriculture-Vision Challenge: Methods and Results. CoRR abs/2004.09754 (2020) - [i33]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Multi-view Self-Constructing Graph Convolutional Networks with Adaptive Class Weighting Loss for Semantic Segmentation. CoRR abs/2004.10327 (2020) - [i32]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation. CoRR abs/2009.01599 (2020) - [i31]Kristoffer Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen:
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series. CoRR abs/2010.11310 (2020) - [i30]Ahcène Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen:
Joint Optimization of an Autoencoder for Clustering and Embedding. CoRR abs/2012.03740 (2020)
2010 – 2019
- 2019
- [j29]Primoz Kocbek, Nino Fijacko, Cristina Soguero-Ruíz, Karl Øyvind Mikalsen, Uros Maver, Petra Povalej Brzan, Andraz Stozer, Robert Jenssen, Stein Olav Skrøvseth, Gregor Stiglic:
Maximizing Interpretability and Cost-Effectiveness of Surgical Site Infection (SSI) Predictive Models Using Feature-Specific Regularized Logistic Regression on Preoperative Temporal Data. Comput. Math. Methods Medicine 2019: 2059851:1-2059851:13 (2019) - [j28]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen:
Deep divergence-based approach to clustering. Neural Networks 113: 91-101 (2019) - [j27]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Robert Jenssen:
Noisy multi-label semi-supervised dimensionality reduction. Pattern Recognit. 90: 257-270 (2019) - [j26]Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
Learning representations of multivariate time series with missing data. Pattern Recognit. 96 (2019) - [c42]Daniel J. Trosten, Andreas Storvik Strauman, Michael Kampffmeyer, Robert Jenssen:
Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series. ICASSP 2019: 3257-3261 - [c41]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Road Mapping in Lidar Images Using a Joint-Task Dense Dilated Convolutions Merging Network. IGARSS 2019: 5041-5044 - [c40]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images. JURSE 2019: 1-4 - [i29]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen:
Deep Divergence-Based Approach to Clustering. CoRR abs/1902.04981 (2019) - [i28]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Robert Jenssen:
Noisy multi-label semi-supervised dimensionality reduction. CoRR abs/1902.07517 (2019) - [i27]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Learning Latent Representations of Bank Customers With The Variational Autoencoder. CoRR abs/1903.06580 (2019) - [i26]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
Time series cluster kernels to exploit informative missingness and incomplete label information. CoRR abs/1907.05251 (2019) - [i25]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images. CoRR abs/1908.11799 (2019) - [i24]Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg:
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network. CoRR abs/1909.04588 (2019) - [i23]Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer, Shujian Yu, José C. Príncipe, Robert Jenssen:
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels. CoRR abs/1909.11396 (2019) - [i22]Van Nhan Nguyen, Robert Jenssen, Davide Roverso:
LS-Net: Fast Single-Shot Line-Segment Detector. CoRR abs/1912.09532 (2019) - 2018
- [j25]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
The deep kernelized autoencoder. Appl. Soft Comput. 71: 816-825 (2018) - [j24]Jonas Nordhaug Myhre, Karl Øyvind Mikalsen, Sigurd Løkse, Robert Jenssen:
Robust clustering using a kNN mode seeking ensemble. Pattern Recognit. 76: 491-505 (2018) - [j23]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, Robert Jenssen:
Time series cluster kernel for learning similarities between multivariate time series with missing data. Pattern Recognit. 76: 569-581 (2018) - [j22]Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen:
Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 11(6): 1758-1768 (2018) - [c39]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz, Robert Jenssen:
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. BHI 2018: 307-310 - [c38]Mads A. Hansen, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz, Robert Jenssen:
Towards deep anchor learning. BHI 2018: 315-318 - [c37]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Inmaculada Mora-Jiménez, Isabel Caballero-López-Fando, Robert Jenssen:
Using multi-anchors to identify patients suffering from multimorbidities. BIBM 2018: 1514-1521 - [c36]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. ESANN 2018 - [c35]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Bidirectional deep-readout echo state networks. ESANN 2018 - [c34]Sigurd Løkse, Robert Jenssen:
Ranking Using Transition Probabilities Learned from Multi-Attribute Data. ICASSP 2018: 2851-2855 - [c33]Qinghui Liu, Arnt-Børre Salberg, Robert Jenssen:
A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images. IGARSS 2018: 6943-6946 - [c32]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty Modeling and interpretability in Convolutional Neural Networks for Polyp Segmentation. MLSP 2018: 1-6 - [i21]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Reservoir computing approaches for representation and classification of multivariate time series. CoRR abs/1803.07870 (2018) - [i20]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Filippo Maria Bianchi, Arthur Revhaug, Robert Jenssen:
An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples. CoRR abs/1803.07879 (2018) - [i19]Shujian Yu, Robert Jenssen, José C. Príncipe:
Understanding Convolutional Neural Network Training with Information Theory. CoRR abs/1804.06537 (2018) - [i18]Filippo Maria Bianchi, Lorenzo Livi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen:
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders. CoRR abs/1805.03473 (2018) - [i17]Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen:
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder. CoRR abs/1806.02538 (2018) - [i16]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
The Deep Kernelized Autoencoder. CoRR abs/1807.07868 (2018) - [i15]Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen:
Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps. CoRR abs/1807.10584 (2018) - [i14]Shujian Yu, Luis Gonzalo Sánchez Giraldo, Robert Jenssen, José C. Príncipe:
Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional. CoRR abs/1808.07912 (2018) - [i13]Daniel J. Trosten, Andreas Storvik Strauman, Michael Kampffmeyer, Robert Jenssen:
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series. CoRR abs/1811.12050 (2018) - 2017
- [b1]Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, Robert Jenssen:
Recurrent Neural Networks for Short-Term Load Forecasting - An Overview and Comparative Analysis. Springer Briefs in Computer Science, Springer 2017, ISBN 978-3-319-70337-4, pp. 1-72 - [j21]Karl Øyvind Mikalsen, Cristina Soguero-Ruíz, Kasper Jensen, Kristian Hindberg, Mads Gran, Arthur Revhaug, Rolv-Ole Lindsetmo, Stein Olav Skrøvseth, Fred Godtliebsen, Robert Jenssen:
Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput. Methods Programs Biomed. 152: 105-114 (2017) - [j20]Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen:
Training Echo State Networks with Regularization Through Dimensionality Reduction. Cogn. Comput. 9(3): 364-378 (2017) - [j19]Katalin Blix, Gustau Camps-Valls, Robert Jenssen:
Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10(4): 1265-1277 (2017) - [j18]Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen, Luis Gómez-Chova, Gustau Camps-Valls:
Optimized Kernel Entropy Components. IEEE Trans. Neural Networks Learn. Syst. 28(6): 1466-1472 (2017) - [c31]Jonas Nordhaug Myhre, Michael Kampffmeyer, Robert Jenssen:
Density ridge manifold traversal. ICASSP 2017: 2342-2346 - [c30]Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen:
Urban land cover classification with missing data using deep convolutional neural networks. IGARSS 2017: 5161-5164 - [c29]Filippo Maria Bianchi, Lorenzo Livi, Robert Jenssen, Cesare Alippi:
Critical echo state network dynamics by means of Fisher information maximization. IJCNN 2017: 852-858 - [c28]Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen:
Temporal overdrive recurrent neural network. IJCNN 2017: 4275-4282 - [c27]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen:
Deep divergence-based clustering. MLSP 2017: 1-6 - [c26]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, Robert Jenssen:
The time series cluster kernel. MLSP 2017: 1-6 - [c25]Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano B. Serpico, Grégoire Mercier:
A Clustering Approach to Heterogeneous Change Detection. SCIA (2) 2017: 181-192 - [c24]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
Deep Kernelized Autoencoders. SCIA (1) 2017: 419-430 - [c23]Sigurd Løkse, Filippo Maria Bianchi, Arnt-Børre Salberg, Robert Jenssen:
Spectral Clustering Using PCKID - A Probabilistic Cluster Kernel for Incomplete Data. SCIA (1) 2017: 431-442 - [i12]Filippo Maria Bianchi, Michael Kampffmeyer, Enrico Maiorino, Robert Jenssen:
Temporal Overdrive Recurrent Neural Network. CoRR abs/1701.05159 (2017) - [i11]Michael Kampffmeyer, Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen, Lorenzo Livi:
Deep Kernelized Autoencoders. CoRR abs/1702.02526 (2017) - [i10]Luigi Tommaso Luppino, Stian Normann Anfinsen, Gabriele Moser, Robert Jenssen, Filippo Maria Bianchi, Sebastiano B. Serpico, Grégoire Mercier:
A clustering approach to heterogeneous change detection. CoRR abs/1702.03176 (2017) - [i9]Karl Øyvind Mikalsen, Filippo Maria Bianchi, Cristina Soguero-Ruíz, Robert Jenssen:
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data. CoRR abs/1704.00794 (2017) - [i8]Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, Robert Jenssen:
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting. CoRR abs/1705.04378 (2017) - [i7]Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen:
Urban Land Cover Classification with Missing Data Using Deep Convolutional Neural Networks. CoRR abs/1709.07383 (2017) - [i6]Filippo Maria Bianchi, Karl Øyvind Mikalsen, Robert Jenssen:
Learning compressed representations of blood samples time series with missing data. CoRR abs/1710.07547 (2017) - [i5]Filippo Maria Bianchi, Simone Scardapane, Sigurd Løkse, Robert Jenssen:
Bidirectional deep echo state networks. CoRR abs/1711.06509 (2017) - [i4]Andreas Storvik Strauman, Filippo Maria Bianchi, Karl Øyvind Mikalsen, Michael Kampffmeyer, Cristina Soguero-Ruíz, Robert Jenssen:
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks. CoRR abs/1711.06516 (2017) - 2016
- [j17]Cristina Soguero-Ruíz, Kristian Hindberg, Inmaculada Mora-Jiménez, José Luis Rojo-Álvarez, Stein Olav Skrøvseth, Fred Godtliebsen, Kim Mortensen, Arthur Revhaug, Rolv-Ole Lindsetmo, Knut Magne Augestad, Robert Jenssen:
Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. J. Biomed. Informatics 61: 87-96 (2016) - [j16]Cristina Soguero-Ruíz, Kristian Hindberg, José Luis Rojo-Álvarez, Stein Olav Skrøvseth, Fred Godtliebsen, Kim Mortensen, Arthur Revhaug, Rolv-Ole Lindsetmo, Knut Magne Augestad, Robert Jenssen:
Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records. IEEE J. Biomed. Health Informatics 20(5): 1404-1415 (2016) - [c22]Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen:
Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks. CVPR Workshops 2016: 680-688 - [p3]Vidar V. Vikjord, Robert Jenssen:
Information Theoretic Clustering using a k-Nearest Neighbors-based Divergence Measure. Handbook of Pattern Recognition and Computer Vision 2016: 69-88 - [i3]Emma Izquierdo-Verdiguier, Valero Laparra, Robert Jenssen, Luis Gómez-Chova, Gustau Camps-Valls:
Optimized Kernel Entropy Components. CoRR abs/1603.02806 (2016) - [i2]Sigurd Løkse, Filippo Maria Bianchi, Robert Jenssen:
Training Echo State Networks with Regularization through Dimensionality Reduction. CoRR abs/1608.04622 (2016) - [i1]Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi, Robert Jenssen:
Multiplex visibility graphs to investigate recurrent neural networks dynamics. CoRR abs/1609.03068 (2016) - 2015
- [j15]Emma Izquierdo-Verdiguier, Robert Jenssen, Luis Gómez-Chova, Gustavo Camps-Valls:
Spectral clustering with the probabilistic cluster kernel. Neurocomputing 149: 1299-1304 (2015) - [c21]Cristina Soguero-Ruíz, Fei Wang, Robert Jenssen, Knut Magne Augestad, José Luis Rojo-Álvarez, Inmaculada Mora-Jiménez, Rolv-Ole Lindsetmo, Stein Olav Skrøvseth:
Data-driven Temporal Prediction of Surgical Site Infection. AMIA 2015 - [c20]Katalin Blix, Gustau Camps-Valls, Robert Jenssen:
Sensitivity analysis of Gaussian processes for oceanic chlorophyll prediction. IGARSS 2015: 996-999 - [c19]Cristina Soguero-Ruíz, Robert Jenssen:
Kernel covariance series smoothing. MLSP 2015: 1-6 - [c18]Jonas Nordhaug Myhre, Karl Øyvind Mikalsen, Sigurd Løkse, Robert Jenssen:
Consensus Clustering Using kNN Mode Seeking. SCIA 2015: 175-186 - 2014
- [j14]Vidar Vikjord, Robert Jenssen:
Information theoretic clustering using a k-nearest neighbors approach. Pattern Recognit. 47(9): 3070-3081 (2014) - [c17]Cristina Soguero-Ruíz, Kristian Hindberg, José Luis Rojo-Álvarez, Stein Olav Skrøvseth, Fred Godtliebsen, Kim Mortensen, Arthur Revhaug, Rolv-Ole Lindsetmo, Inmaculada Mora-Jiménez, Knut Magne Augestad, Robert Jenssen:
Bootstrap resampling feature selection and Support Vector Machine for early detection of Anastomosis Leakage. BHI 2014: 577-580 - [c16]Jørgen Andreas Agersborg, Robert Jenssen:
Mean Shift Spectral Clustering using Kernel Entropy Component Analysis. MLSP 2014: 1-6 - 2013
- [j13]Robert Jenssen:
Entropy-Relevant Dimensions in the Kernel Feature Space: Cluster-Capturing Dimensionality Reduction. IEEE Signal Process. Mag. 30(4): 30-39 (2013) - [j12]Robert Jenssen:
Mean Vector Component Analysis for Visualization and Clustering of Nonnegative Data. IEEE Trans. Neural Networks Learn. Syst. 24(10): 1553-1564 (2013) - [c15]Robert Jenssen:
Mean vector component analysis: A new approach to un-centered PCA for non-negative data. MLSP 2013: 1-6 - [c14]Vidar Vikjord, Robert Jenssen:
A new information theoretic clustering algorithm using k-nn. MLSP 2013: 1-6 - 2012
- [c13]Jonas Nordhaug Myhre, Robert Jenssen:
Mixture weight influence on kernel entropy component analysis and semi-supervised learning using the Lasso. MLSP 2012: 1-6 - 2011
- [c12]Luis Gómez-Chova, Robert Jenssen, Gustavo Camps-Valls:
Kernel entropy component analysis in remote sensing data clustering. IGARSS 2011: 3728-3731 - [c11]Robert Jenssen:
Kernel entropy component analysis: New theory and semi-supervised learning. MLSP 2011: 1-6 - [c10]Robert Jenssen, Marius Kloft, Sören Sonnenburg, Alexander Zien, Klaus-Robert Müller:
A new scatter-based multi-class support vector machine. MLSP 2011: 1-6 - 2010
- [j11]Robert Jenssen:
Kernel Entropy Component Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32(5): 847-860 (2010) - [j10]Robert Jenssen, José Carlos Príncipe:
Information theoretic clustering. Scholarpedia 5(10): 5189 (2010) - [p2]Robert Jenssen, Sudhir Rao:
Clustering with ITL Principles. Information Theoretic Learning 2010: 263-298 - [p1]Jian-Wu Xu, Robert Jenssen, António R. C. Paiva, Il Park:
A Reproducing Kernel Hilbert Space Framework for ITL. Information Theoretic Learning 2010: 351-384
2000 – 2009
- 2009
- [c9]Robert Jenssen, Ola Storås:
Kernel Entropy Component Analysis Pre-images for Pattern Denoising. SCIA 2009: 626-635 - [e1]Arnt-Børre Salberg, Jon Yngve Hardeberg, Robert Jenssen:
Image Analysis, 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings. Lecture Notes in Computer Science 5575, Springer 2009, ISBN 978-3-642-02229-6 [contents] - 2008
- [j9]Robert Jenssen, Torbjørn Eltoft:
A new information theoretic analysis of sum-of-squared-error kernel clustering. Neurocomputing 72(1-3): 23-31 (2008) - [j8]Umut Ozertem, Deniz Erdogmus, Robert Jenssen:
Mean shift spectral clustering. Pattern Recognit. 41(6): 1924-1938 (2008) - 2007
- [j7]Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II, José C. Príncipe, Torbjørn Eltoft:
Information cut for clustering using a gradient descent approach. Pattern Recognit. 40(3): 796-806 (2007) - [j6]Robert Jenssen, Deniz Erdogmus, José C. Príncipe, Torbjørn Eltoft:
The Laplacian Classifier. IEEE Trans. Signal Process. 55(7-1): 3262-3271 (2007) - 2006
- [j5]Robert Jenssen, José C. Príncipe, Deniz Erdogmus, Torbjørn Eltoft:
The Cauchy-Schwarz divergence and Parzen windowing: Connections to graph theory and Mercer kernels. J. Frankl. Inst. 343(6): 614-629 (2006) - [j4]Umut Ozertem, Deniz Erdogmus, Robert Jenssen:
Spectral feature projections that maximize Shannon mutual information with class labels. Pattern Recognit. 39(7): 1241-1252 (2006) - [j3]Robert Jenssen, Torbjørn Eltoft, Deniz Erdogmus, José C. Príncipe:
Some Equivalences between Kernel Methods and Information Theoretic Methods. J. VLSI Signal Process. 45(1-2): 49-65 (2006) - [j2]Deniz Erdogmus, Robert Jenssen, Yadunandana N. Rao, José C. Príncipe:
Gaussianization: An Efficient Multivariate Density Estimation Technique for Statistical Signal Processing. J. VLSI Signal Process. 45(1-2): 67-83 (2006) - [c8]Robert Jenssen, Deniz Erdogmus, José C. Príncipe:
Information Theoretic Angle-Based Spectral Clustering: A Theoretical Analysis and an Algorithm. IJCNN 2006: 4904-4911 - [c7]Robert Jenssen, Torbjørn Eltoft, Mark A. Girolami, Deniz Erdogmus:
Kernel Maximum Entropy Data Transformation and an Enhanced Spectral Clustering Algorithm. NIPS 2006: 633-640 - 2005
- [c6]Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II, José Carlos Príncipe, Torbjørn Eltoft:
Optimizing the Cauchy-Schwarz PDF Distance for Information Theoretic, Non-parametric Clustering. EMMCVPR 2005: 34-45 - [c5]Jian-Wu Xu, Deniz Erdogmus, Robert Jenssen, José C. Príncipe:
An information-theoretic perspective to kernel independent components analysis. ICASSP (5) 2005: 249-252 - [c4]Robert Jenssen, Deniz Erdogmus, José C. Príncipe, Torbjørn Eltoft:
The Laplacian spectral classifier. ICASSP (5) 2005: 325-328 - 2004
- [c3]Robert Jenssen, Deniz Erdogmus, José Carlos Príncipe, Torbjørn Eltoft:
The Laplacian PDF Distance: A Cost Function for Clustering in a Kernel Feature Space. NIPS 2004: 625-632 - 2003
- [j1]Robert Jenssen, Torbjørn Eltoft:
Independent component analysis for texture segmentation. Pattern Recognit. 36(10): 2301-2315 (2003) - [c2]Robert Jenssen, Deniz Erdogmus, Kenneth E. Hild II, José Carlos Príncipe, Torbjørn Eltoft:
Information Force Clustering Using Directed Trees. EMMCVPR 2003: 68-82 - [c1]Robert Jenssen, José C. Príncipe, Torbjørn Eltoft:
Information cut and information forces for clustering. NNSP 2003: 459-468
Coauthor Index
[j57] [j55] [j54] [j53] [c66] [j52] [j51] [j50] [j49] [j48] [c61] [c60] [c59] [c58] [i58] [i57] [i56] [j46] [j45] [j44] [j43] [c57] [c56] [c55] [c54] [i55] [i54] [i53] [i51] [i49] [i48] [j41] [j40] [j39] [j36] [c50] [c48] [i46] [i44] [i43] [i42] [i41] [j33] [j32] [j30] [c47] [c46] [c44] [c43] [i40] [i39] [i37] [i36] [i35] [i34] [i33] [i32] [i31] [i30] [j28] [j26] [c42] [c41] [c40] [i29] [i27] [i25] [i24] [i23] [j25] [j22] [c39] [c38] [c32] [i18] [i17] [i16] [i15] [i13] [b1] [c31] [c30] [c28] [c27] [c24] [i12] [i11] [i8] [i7] [i4] [c22]
José C. Príncipe
aka: José Carlos Príncipe
aka: José Carlos Príncipe
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