33 citations · 50 across the 9 of their papers we have counts for
9 papers
Towards Continual Test-Time Adaptation of Vision-Language Models in Open-Vocabulary Semantic Segmentation
Chandler Timm C. Doloriel, Yunbei Zhang, Sarthak Kumar Maharana +5
Open-vocabulary semantic segmentation (OVSS) relies on vision-language alignment to recognize arbitrary text-defined categories, yet this alignment is fragile under continual test-…
Continual Test-Time Adaptation via Entropy Sensitivity-Guidance in Strict Online Setting
Chandler Timm C. Doloriel, Yunbei Zhang, Muhammad Salman Siddiqui +4
Test-time adaptation (TTA) promises robustness under distribution shift by updating a pretrained model on unlabeled test data, but strict online TTA with batch size one and no acce…
Towards Sustainable Universal Deepfake Detection with Frequency-Domain Masking
Chandler Timm C. Doloriel, Habib Ullah, Kristian Hovde Liland +2
Universal deepfake detection aims to identify AI-generated images across a broad range of generative models, including unseen ones. This requires robust generalization to new and u…
General Effect Modelling (GEM) -- Part 3. GEM applied on proteome data of cerebrospinal fluid of multiple sclerosis and clinically isolated syndrome
Ellen Færgestad Mosleth, Kjell-Morten Myhr, Christian Alexander Vedeler +4
The novel data analytical platform General Effect Modelling (GEM), is an umbrella platform covering different data analytical methods that handle data with multiple design variable…
General Effect Modelling (GEM) -- Part 2. Multivariate GEM applied to gene expression data of type 2 diabetes detects information that is lost by univariate validation
Ellen Færgestad Mosleth, Simon Erling Nitter Dankel, Gunnar Mellgren +7
General Effect Modelling (GEM) is an umbrella over different methods that utilise effects in the analyses of data with multiple design variables and multivariate responses. To demo…
General Effect Modelling (GEM) -- Part 1. Method description
Ellen Færgestad Mosleth, Kristian Hovde Liland
We present a flexible tool, called General Effect Modelling (GEM), for the analysis of any multivariate data influenced by one or more qualitative (categorical) or quantitative (co…
A New Formula for Faster Computation of the K-Fold Cross-Validation and Good Regularisation Parameter Values in Ridge Regression
Kristian Hovde Liland, Joakim Skogholt, Ulf Geir Indahl
In the present paper, we prove a new theorem, resulting in an update formula for linear regression model residuals calculating the exact k-fold cross-validation residuals for any c…
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks
Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh +4
In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset.…
RENT -- Repeated Elastic Net Technique for Feature Selection
Anna Jenul, Stefan Schrunner, Kristian Hovde Liland +3
Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing pr…