Data representation and feature selection for colorimetric sensor arrays used as explosives detectors. - Tommy S. Alstrøm; Jan Larsen; Natalie V. Kostesha; Mogens H. Jakobsen; Anja Boisen (MLSP, 2011)

Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors. - Tommy S. Alstrøm; Raviv Raich; Natalie V. Kostesha; Jan Larsen (ICASSP, 2012)

Haussdorff and hellinger for colorimetric sensor array classification. - Tommy S. Alstrøm; Bjørn Sand Jensen; Mikkel N. Schmidt; Natalie V. Kostesha; Jan Larsen (MLSP, 2012)

Improving the robustness of Surface Enhanced Raman Spectroscopy based sensors by Bayesian Non-negative Matrix Factorization. - Tommy S. Alstrøm; Kasper B. Frohling; Jan Larsen; Mikkel N. Schmidt; Michael Bache; Michael S. Schmidt; Mogens H. Jakobsen; Anja Boisen (MLSP, 2014)

Click chemistry based biomolecular conjugation monitoring using surface-enhanced Raman spectroscopy mapping. - Mirkó Palla; Shiv Kumar; Zengmin Li; Steffen Jockusch; James J. Russo; Jingyue Ju; Filippo G. Bosco; Tomas Rindzevicius; Tommy S. Alstrøm; Michael S. Schmidt; Anja Boisen (IEEE SENSORS, 2016)

A pseudo-Voigt component model for high-resolution recovery of constituent spectra in Raman spectroscopy. - Tommy S. Alstrøm; Mikkel N. Schmidt; Tomas Rindzevicius; Anja Boisen; Jan Larsen (ICASSP, 2017)

Peak Detection and Baseline Correction Using a Convolutional Neural Network. - Mikkel N. Schmidt; Tommy S. Alstrøm; Marcus Svendstorp; Jan Larsen (ICASSP, 2019)

Insights Into The Behaviour Of Multi-Task Deep Neural Networks For Medical Image Segmentation. - Lukasz T. Bienias; Juanjo R. Guillamón; Line H. Nielsen; Tommy S. Alstrøm (MLSP, 2019)

A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. - Maximillian Fornitz Vording; Peter O. Okeyo; Juan J. R. Guillamón; Peter E. Larsen; Mikkel N. Schmidt; Tommy S. Alstrøm (MLSP, 2019)

On uncertainty estimation in active learning for image segmentation. - Bo Li 119; Tommy Sonne Alstrøm (CoRR, 2020)

Road Roughness Estimation Using Machine Learning. - Milena Bajic; Shahrzad M. Pour; Asmus Skar; Matteo Pettinari; Eyal Levenberg; Tommy Sonne Alstrøm (CoRR, 2021)

Raman Spectrum Matching with Contrastive Representation Learning. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm (CoRR, 2022)

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CoRR, 2022)

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data. - Bo Li 119; Mikkel N. Schmidt; Tommy S. Alstrøm; Sebastian U. Stich (CVPR, 2023)

On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems. - Philippe Gonzalez; Tommy Sonne Alstrøm; Tobias May (ICASSP, 2023)

Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series. - Thea Brüsch; Mikkel N. Schmidt; Tommy S. Alstrøm (MLSP, 2023)

Amortized Variational Peak Fitting For Spectroscopic Data. - David Frich Hansen; Tommy Sonne Alstrøm; Mikkel N. Schmidt (MLSP, 2023)

On convex conceptual regions in deep network representations. - Lenka Tetková; Thea Brüsch; Teresa Karen Scheidt; Fabian Martin Mager; Rasmus Ørtoft Aagaard; Jonathan Foldager; Tommy Sonne Alstrøm; Lars Kai Hansen (CoRR, 2023)

Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments. - Philippe Gonzalez; Tommy Sonne Alstrøm; Tobias May (IEEE ACM Trans. Audio Speech Lang. Process., 2023)

Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler. - Philippe Gonzalez; Zheng-Hua Tan; Jan Østergaard; Jesper Jensen 001; Tommy Sonne Alstrøm; Tobias May (ICASSP, 2024)

An improved analysis of per-sample and per-update clipping in federated learning. - Bo Li 119; Xiaowen Jiang; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (ICLR, 2024)

Explaining time series models using frequency masking. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Tommy S. Alstrøm; Robert Jenssen (CoRR, 2024)

Contrastive random lead coding for channel-agnostic self-supervision of biosignals. - Thea Brüsch; Mikkel N. Schmidt; Tommy S. Alstrøm (CoRR, 2024)

FLEXtime: Filterbank learning for explaining time series. - Thea Brüsch; Kristoffer K. Wickstrøm; Mikkel N. Schmidt; Robert Jenssen; Tommy S. Alstrøm (CoRR, 2024)

The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems. - Philippe Gonzalez; Zheng-Hua Tan; Jan Østergaard; Jesper Jensen 001; Tommy Sonne Alstrøm; Tobias May (IEEE Signal Process. Lett., 2024)

Investigating the Design Space of Diffusion Models for Speech Enhancement. - Philippe Gonzalez; Zheng-Hua Tan; Jan Østergaard; Jesper Jensen 001; Tommy Sonne Alstrøm; Tobias May (IEEE ACM Trans. Audio Speech Lang. Process., 2024)

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. - Bo Li 119; Yasin Esfandiari; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Sebastian U. Stich (Trans. Mach. Learn. Res., 2024)

Towards Trustworthy AI in Demand Planning: Defining Explainability for Supply Chain Management. - Ruiqi Zhu; Cecilie Christensen; Bahram Zarrin; Per Bækgaard; Tommy Sonne Alstrøm (ICAART (3), 2025)

FreqRISE: Explaining time series using frequency masking. - Thea Brüsch; Kristoffer Knutsen Wickstrøm; Mikkel N. Schmidt; Tommy Sonne Alstrøm; Robert Jenssen (NLDL, 2025)