280 citations · 1.7k across the 60 of their papers we have counts for
8 papers · 1 filter
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels
Kristoffer Wickstrøm, Sigurd Løkse, Michael Kampffmeyer +3
Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization…
Road Mapping In LiDAR Images Using A Joint-Task Dense Dilated Convolutions Merging Network
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
It is important, but challenging, for the forest industry to accurately map roads which are used for timber transport by trucks. In this work, we propose a Dense Dilated Convolutio…
Dense Dilated Convolutions Merging Network for Semantic Mapping of Remote Sensing Images
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen +1
We propose a network for semantic mapping called the Dense Dilated Convolutions Merging Network (DDCM-Net) to provide a deep learning approach that can recognize multi-scale and co…
Time series cluster kernels to exploit informative missingness and incomplete label information
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +2
The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in…
Deep Generative Models for Reject Inference in Credit Scoring
Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
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…
Learning Latent Representations of Bank Customers With The Variational Autoencoder
Rogelio A Mancisidor, Michael Kampffmeyer, Kjersti Aas +1
Learning data representations that reflect the customers' creditworthiness can improve marketing campaigns, customer relationship management, data and process management or the cre…
Noisy multi-label semi-supervised dimensionality reduction
Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +1
Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not ac…
Deep Divergence-Based Approach to Clustering
Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi +3
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminati…