Tommy S. Alstrøm

According to our database, Tommy S. Alstrøm authored at least 26 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity.

Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich

Trans. Mach. Learn. Res., 2024

Investigating the Design Space of Diffusion Models for Speech Enhancement.

Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen, Tommy Sonne Alstrøm, Tobias May

IEEE ACM Trans. Audio Speech Lang. Process., 2024

The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems.

Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen, Tommy Sonne Alstrøm, Tobias May

IEEE Signal Process. Lett., 2024

Contrastive random lead coding for channel-agnostic self-supervision of biosignals.

Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm

CoRR, 2024

Explaining time series models using frequency masking.

Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt, Tommy S. Alstrøm, Robert Jenssen

CoRR, 2024

An improved analysis of per-sample and per-update clipping in federated learning.

Bo Li, Xiaowen Jiang, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler.

Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard, Jesper Jensen, Tommy Sonne Alstrøm, Tobias May

Proceedings of the IEEE International Conference on Acoustics, 2024

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

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

Amortized Variational Peak Fitting For Spectroscopic Data.

David Frich Hansen, Tommy Sonne Alstrøm, Mikkel N. Schmidt

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

Multi-View Self-Supervised Learning For Multivariate Variable-Channel Time Series.

Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm

Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems.

Philippe Gonzalez, Tommy Sonne Alstrøm, Tobias May

Proceedings of the IEEE International Conference on Acoustics, 2023

On the Effectiveness of Partial Variance Reduction in Federated Learning with Heterogeneous Data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022

Partial Variance Reduction improves Non-Convex Federated learning on heterogeneous data.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm, Sebastian U. Stich

CoRR, 2022

Raman Spectrum Matching with Contrastive Representation Learning.

Bo Li, Mikkel N. Schmidt, Tommy S. Alstrøm

CoRR, 2022

2021

Road Roughness Estimation Using Machine Learning.

Milena Bajic, Shahrzad M. Pour, Asmus Skar, Matteo Pettinari, Eyal Levenberg, Tommy Sonne Alstrøm

CoRR, 2021

2020

On uncertainty estimation in active learning for image segmentation.

Bo Li, Tommy Sonne Alstrøm

CoRR, 2020

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

Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 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

Proceedings of the 29th IEEE International Workshop on Machine Learning for Signal Processing, 2019

Peak Detection and Baseline Correction Using a Convolutional Neural Network.

Mikkel N. Schmidt, Tommy S. Alstrøm, Marcus Svendstorp, Jan Larsen

Proceedings of the IEEE International Conference on Acoustics, 2019

2017

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

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

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

Proceedings of the 2016 IEEE SENSORS, Orlando, FL, USA, October 30 - November 3, 2016, 2016

2014

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

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

2012

Haussdorff and hellinger for colorimetric sensor array classification.

Tommy S. Alstrøm, Bjørn Sand Jensen, Mikkel N. Schmidt, Natalie V. Kostesha, Jan Larsen

Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2012

Feature extraction using distribution representation for colorimetric sensor arrays used as explosives detectors.

Tommy S. Alstrøm, Raviv Raich, Natalie V. Kostesha, Jan Larsen

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2011

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

Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011