84 citations · 325 across the 22 of their papers we have counts for
23 papers
Modeling Human Responses by Ordinal Archetypal Analysis
Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom +6
This paper introduces a novel framework for Archetypal Analysis (AA) tailored to ordinal data, particularly from questionnaires. Unlike existing methods, the proposed method, Ordin…
FreqRISE: Explaining time series using frequency masking
Thea Brüsch, Kristoffer Knutsen Wickstrøm, Mikkel N. Schmidt +2
Time-series data are fundamentally important for many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision…
Coherent energy and force uncertainty in deep learning force fields
Peter Bjørn Jørgensen, Jonas Busk, Ole Winther +1
In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quanti…
Multi-view self-supervised learning for multivariate variable-channel time series
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets thro…
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity
Bo Li, Yasin Esfandiari, Mikkel N. Schmidt +2
In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may viol…
Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces
Jonas Busk, Mikkel N. Schmidt, Ole Winther +2
Inexpensive machine learning potentials are increasingly being used to speed up structural optimization and molecular dynamics simulations of materials by iteratively predicting an…
On the effectiveness of partial variance reduction in federated learning with heterogeneous data
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm +1
Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correc…
End-to-End Learning for VCSEL-based Optical Interconnects: State-of-the-Art, Challenges, and Opportunities
Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski +8
Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-c…
Raman Spectrum Matching with Contrastive Representation Learning
Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm
Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference d…
Programmatic Policy Extraction by Iterative Local Search
Rasmus Larsen, Mikkel Nørgaard Schmidt
Reinforcement learning policies are often represented by neural networks, but programmatic policies are preferred in some cases because they are more interpretable, amenable to for…
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks
Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik +3
Data-driven methods based on machine learning have the potential to accelerate computational analysis of atomic structures. In this context, reliable uncertainty estimates are impo…
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
Peter Bjørn Jørgensen, Estefanía Garijo del Río, Mikkel N. Schmidt +1
Computational materials screening studies require fast calculation of the properties of thousands of materials. The calculations are often performed with Density Functional Theory…
Probabilistic PARAFAC2
Philip J. H. Jørgensen, Søren F. V. Nielsen, Jesper L. Hinrich +3
The PARAFAC2 is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example because of difference…
Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials
Peter Bjørn Jørgensen, Karsten Wedel Jacobsen, Mikkel N. Schmidt
Neural message passing on molecular graphs is one of the most promising methods for predicting formation energy and other properties of molecules and materials. In this work we ext…
Scalable Group Level Probabilistic Sparse Factor Analysis
Jesper L. Hinrich, Søren F. V. Nielsen, Nicolai A. B. Riis +6
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation.…
Nonparametric Modeling of Dynamic Functional Connectivity in fMRI Data
Søren F. V. Nielsen, Kristoffer H. Madsen, Rasmus Røge +2
Dynamic functional connectivity (FC) has in recent years become a topic of interest in the neuroimaging community. Several models and methods exist for both functional magnetic res…
Bayesian Dropout
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grou…
Completely random measures for modelling block-structured networks
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the s…
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Tue Herlau, Morten Mørup, Yee Whye Teh +1
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widel…
Non-parametric Bayesian modeling of complex networks
Mikkel N. Schmidt, Morten Mørup
Modeling structure in complex networks using Bayesian non-parametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observ…
Nonparametric Bayesian models of hierarchical structure in complex networks
Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can…
The Infinite Degree Corrected Stochastic Block Model
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statisticall…
Infinite Multiple Membership Relational Modeling for Complex Networks
Morten Mørup, Mikkel N. Schmidt, Lars Kai Hansen
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this pap…