28 citations · 121 across the 16 of their papers we have counts for
17 papers
Learnable Time-Frequency Masks for Explaining Time-Series Classifiers
Theresa Dahl Frehr, Francisco Pelayo, Lukas Raad +3
Time-series explainability remains challenging because discriminative information is often encoded in latent frequency or time-frequency features rather than in the raw signal itse…
On What We Can Learn from Low-Resolution Data
Theresa Dahl Frehr, Niels Henrik Pontoppidan, Hiba Nassar +1
Artificial intelligence systems typically rely on large, centrally collected datasets, a premise that does not hold in many real-world domains such as healthcare and public institu…
FLEXtime: Filterbank learning to explain time series
Thea Brüsch, Kristoffer K. Wickstrøm, Mikkel N. Schmidt +2
State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step; however, many types of time series are diff…
A comprehensive evaluation of pretraining strategies for channel-agnostic contrastive self-supervision of biosignals
Thea Brüsch, Mikkel N. Schmidt, Tommy S. Alstrøm
Contrastive learning yields impressive results for self-supervision in computer vision. The approach relies on the creation of positive pairs, something which is often achieved thr…
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…
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of trainin…
Diffusion-Based Speech Enhancement in Matched and Mismatched Conditions Using a Heun-Based Sampler
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performanc…
Investigating the Design Space of Diffusion Models for Speech Enhancement
Philippe Gonzalez, Zheng-Hua Tan, Jan Østergaard +3
Diffusion models are a new class of generative models that have shown outstanding performance in image generation literature. As a consequence, studies have attempted to apply diff…
Assessing the Generalization Gap of Learning-Based Speech Enhancement Systems in Noisy and Reverberant Environments
Philippe Gonzalez, Tommy Sonne Alstrøm, Tobias May
The acoustic variability of noisy and reverberant speech mixtures is influenced by multiple factors, such as the spectro-temporal characteristics of the target speaker and the inte…
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…
On convex decision regions in deep network representations
Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt +5
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{ä}rdenfors' conceptual spaces is a…
On Batching Variable Size Inputs for Training End-to-End Speech Enhancement Systems
Philippe Gonzalez, Tommy Sonne Alstrøm, Tobias May
The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization…
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…
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…
Road Roughness Estimation Using Machine Learning
Milena Bajic, Shahrzad M. Pour, Asmus Skar +3
Road roughness is a very important road condition for the infrastructure, as the roughness affects both the safety and ride comfort of passengers. The roads deteriorate over time w…
On uncertainty estimation in active learning for image segmentation
Bo Li, Tommy Sonne Alstrøm
Uncertainty estimation is important for interpreting the trustworthiness of machine learning models in many applications. This is especially critical in the data-driven active lear…