132 citations · 521 across the 32 of their papers we have counts for
34 papers
Concept-based explainability for an EEG transformer model
Anders Gjølbye, William Lehn-Schiøler, Áshildur Jónsdóttir +2
Deep learning models are complex due to their size, structure, and inherent randomness in training procedures. Additional complexity arises from the selection of datasets and induc…
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts
Raluca Alexandra Fetic, Mikkel Jordahn, Lucas Chaves Lima +4
For a multilingual podcast streaming service, it is critical to be able to deliver relevant content to all users independent of language. Podcast content relevance is conventionall…
Generalization by design: Shortcuts to Generalization in Deep Learning
Petr Taborsky, Lars Kai Hansen
We take a geometrical viewpoint and present a unifying view on supervised deep learning with the Bregman divergence loss function - this entails frequent classification and predict…
A simple defense against adversarial attacks on heatmap explanations
Laura Rieger, Lars Kai Hansen
With machine learning models being used for more sensitive applications, we rely on interpretability methods to prove that no discriminating attributes were used for classification…
Client Adaptation improves Federated Learning with Simulated Non-IID Clients
Laura Rieger, Rasmus M. Th. Høegh, Lars K. Hansen
We present a federated learning approach for learning a client adaptable, robust model when data is non-identically and non-independently distributed (non-IID) across clients. By s…
Probabilistic Decoupling of Labels in Classification
Jeppe Nørregaard, Lars Kai Hansen
In this paper we develop a principled, probabilistic, unified approach to non-standard classification tasks, such as semi-supervised, positive-unlabelled, multi-positive-unlabelled…
IROF: a low resource evaluation metric for explanation methods
Laura Rieger, Lars Kai Hansen
The adoption of machine learning in health care hinges on the transparency of the used algorithms, necessitating the need for explanation methods. However, despite a growing litera…
Robustness analytics to data heterogeneity in edge computing
Jia Qian, Lars Kai Hansen, Xenofon Fafoutis +2
Federated Learning is a framework that jointly trains a model \textit{with} complete knowledge on a remotely placed centralized server, but \textit{without} the requirement of acce…
The Complexity of Social Media Response: Statistical Evidence For One-Dimensional Engagement Signal in Twitter
Damian Konrad Kowalczyk, Lars Kai Hansen
Many years after online social networks exceeded our collective attention, social influence is still built on attention capital. Quality is not a prerequisite for viral spreading,…
Active Learning Solution on Distributed Edge Computing
Jia Qian, Sayantan Sengupta, Lars Kai Hansen
Industry 4.0 becomes possible through the convergence between Operational and Information Technologies. All the requirements to realize the convergence is integrated on the Fog Pla…
Probabilistic Decoupling of Labels in Classification
Jeppe Nørregaard, Lars Kai Hansen
We investigate probabilistic decoupling of labels supplied for training, from the underlying classes for prediction. Decoupling enables an inference scheme general enough to implem…
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Niels Bruun Ipsen, Lars Kai Hansen
How does missing data affect our ability to learn signal structures? It has been shown that learning signal structure in terms of principal components is dependent on the ratio of…
Aggregating explanation methods for stable and robust explainability
Laura Rieger, Lars Kai Hansen
Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation. Our contrib…
Multi-View Bayesian Correlated Component Analysis
Simon Kamronn, Andreas Trier Poulsen, Lars Kai Hansen
Correlated component analysis as proposed by Dmochowski et al. (2012) is a tool for investigating brain process similarity in the responses to multiple views of a given stimulus. C…
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals…
Adaptive Smoothing in fMRI Data Processing Neural Networks
Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen
Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data processing pipelines to accurately determine brain activity; among them, the crucial step of spatial smoothin…
Latent Space Oddity: on the Curvature of Deep Generative Models
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
Deep generative models provide a systematic way to learn nonlinear data distributions, through a set of latent variables and a nonlinear "generator" function that maps latent point…
EEG source imaging assists decoding in a face recognition task
Rasmus S. Andersen, Anders U. Eliasen, Nicolai Pedersen +3
EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting tha…
Towards end-to-end optimisation of functional image analysis pipelines
Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen
The study of neurocognitive tasks requiring accurate localisation of activity often rely on functional Magnetic Resonance Imaging, a widely adopted technique that makes use of a pi…
A Locally Adaptive Normal Distribution
Georgios Arvanitidis, Lars Kai Hansen, Søren Hauberg
The multivariate normal density is a monotonic function of the distance to the mean, and its ellipsoidal shape is due to the underlying Euclidean metric. We suggest to replace this…
EEG in the classroom: Synchronised neural recordings during video presentation
Andreas Trier Poulsen, Simon Kamronn, Jacek Dmochowski +2
We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a…
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation
Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen +2
Data augmentation is a key element in training high-dimensional models. In this approach, one synthesizes new observations by applying pre-specified transformations to the original…
Bayesian inference for spatio-temporal spike-and-slab priors
Michael Riis Andersen, Aki Vehtari, Ole Winther +1
In this work, we address the problem of solving a series of underdetermined linear inverse problems subject to a sparsity constraint. We generalize the spike-and-slab prior distrib…
Spatio-temporal Spike and Slab Priors for Multiple Measurement Vector Problems
Michael Riis Andersen, Ole Winther, Lars Kai Hansen
We are interested in solving the multiple measurement vector (MMV) problem for instances, where the underlying sparsity pattern exhibit spatio-temporal structure motivated by the e…