Kjersti Aas

Data Min. Knowl. Discov., July, 2024

Discriminative multimodal learning via conditional priors in generative models.

[BibT_eX]

[DOI]

Neural Networks, January, 2024

2023

A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them.

[BibT_eX]

[DOI]

Lars Henry Berge Olsen

Ingrid Kristine Glad

CoRR, 2023

2022

Generating customer's credit behavior with deep generative models.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2022

Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

2021

Learning latent representations of bank customers with the Variational Autoencoder.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2021

MCCE: Monte Carlo sampling of realistic counterfactual explanations.

[BibT_eX]

[DOI]

CoRR, 2021

groupShapley: Efficient prediction explanation with Shapley values for feature groups.

[BibT_eX]

[DOI]

Annabelle Redelmeier

CoRR, 2021

Explaining predictive models using Shapley values and non-parametric vine copulas.

[BibT_eX]

[DOI]

CoRR, 2021

Explaining individual predictions when features are dependent: More accurate approximations to Shapley values.

[BibT_eX]

[DOI]

Anders Løland

Artif. Intell., 2021

2020

Deep generative models for reject inference in credit scoring.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2020

Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees.

[BibT_eX]

[DOI]

Annabelle Redelmeier

Proceedings of the Machine Learning and Knowledge Extraction, 2020

2019

The evolution of a mobile payment solution network.

[BibT_eX]

[DOI]

Hanne Rognebakke

Netw. Sci., 2019

2018

Predicting mortgage default using convolutional neural networks.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder.

[BibT_eX]

[DOI]

CoRR, 2018

2016

Structure learning in Bayesian Networks using regular vines.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2016

2010

On the simplified pair-copula construction - Simply useful or too simplistic?

[BibT_eX]

[DOI]

Ingrid Hobæk Haff

Arnoldo Frigessi

J. Multivar. Anal., 2010

1999

Applications of hidden Markov chains in image analysis.

[BibT_eX]

[DOI]

Ragnar Bang Huseby

Pattern Recognit., 1999

1997

Decoding bar codes from human-readable characters.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 1997

1996

Text page recognition using Grey-level features and hidden Markov models.

[BibT_eX]

[DOI]

Pattern Recognit., 1996

Automatic can separation.

[BibT_eX]

[DOI]

Otto Milvang

Proceedings of the 13th International Conference on Pattern Recognition, 1996

1995

Tools for interactive map conversion and vectorization.

[BibT_eX]

[DOI]

Hans Koren

Proceedings of the Third International Conference on Document Analysis and Recognition, 1995

Tools for Automatic Recognition of Character Strings in Maps.

[BibT_eX]

[DOI]

Marit Holden

Proceedings of the Computer Analysis of Images and Patterns, 6th International Conference, 1995

Text Recogniton from Grey Level Images Using Hidden Markovc Models.

[BibT_eX]

[DOI]

Tove Andersen

Proceedings of the Computer Analysis of Images and Patterns, 6th International Conference, 1995

1992

Combining range and intensity data with a hidden Markov model.

[BibT_eX]

[DOI]

Ragnar Bang Huseby

Gutorm Thomas Hogasen

Geir Storvik