Recurrent Deep Divergence-based Clustering for Simultaneous Feature Learning and Clustering of Variable Length Time Series. - Daniel J. Trosten; Andreas Storvik Strauman; Michael Kampffmeyer; Robert Jenssen (ICASSP, 2019)

Unsupervised Feature Extraction - A CNN-Based Approach. - Daniel J. Trosten; Puneet Sharma (SCIA, 2019)

Deep Image Clustering with Tensor Kernels and Unsupervised Companion Objectives. - Daniel J. Trosten; Michael C. Kampffmeyer; Robert Jenssen (CoRR, 2020)

Reconsidering Representation Alignment for Multi-View Clustering. - Daniel J. Trosten; Sigurd Løkse; Robert Jenssen; Michael Kampffmeyer (CVPR, 2021)

Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective. - Daniel J. Trosten; Robert Jenssen; Michael C. Kampffmeyer (NLDL, 2021)

RELAX: Representation Learning Explainability. - Kristoffer K. Wickstrøm; Daniel J. Trosten; Sigurd Løkse; Karl Øyvind Mikalsen; Michael C. Kampffmeyer; Robert Jenssen (CoRR, 2021)

Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings. - Daniel J. Trosten; Rwiddhi Chakraborty; Sigurd Løkse; Kristoffer Knutsen Wickstrøm; Robert Jenssen; Michael C. Kampffmeyer (CVPR, 2023)

On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering. - Daniel J. Trosten; Sigurd Løkse; Robert Jenssen; Michael C. Kampffmeyer (CVPR, 2023)

Questionable Practices in Methodological Deep Learning Research. - Daniel J. Trosten (NLDL, 2023)

RELAX: Representation Learning Explainability. - Kristoffer K. Wickstrøm; Daniel J. Trosten; Sigurd Løkse; Ahcène Boubekki; Karl Øyvind Mikalsen; Michael C. Kampffmeyer; Robert Jenssen (Int. J. Comput. Vis., 2023)

Leveraging tensor kernels to reduce objective function mismatch in deep clustering. - Daniel J. Trosten; Sigurd Løkse; Robert Jenssen; Michael Kampffmeyer (Pattern Recognit., 2024)