Zobrazeno 1 - 10
of 439
pro vyhledávání: '"Alstrom, P."'
Autor:
Brüsch, Thea, Wickstrøm, Kristoffer K., Schmidt, Mikkel N., Jenssen, Robert, Alstrøm, Tommy S.
State-of-the-art methods for explaining predictions based on time series are built on learning an instance-wise saliency mask for each time step. However, for many types of time series, the salient information is found in the frequency domain. Adopti
Externí odkaz:
http://arxiv.org/abs/2411.05841
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 through augmentations. However, for multivariate time series effective au
Externí odkaz:
http://arxiv.org/abs/2410.19842
Autor:
Brüsch, Thea, Wickstrøm, Kristoffer K., Schmidt, Mikkel N., Alstrøm, Tommy S., Jenssen, Robert
Time series data is fundamentally important for describing many critical domains such as healthcare, finance, and climate, where explainable models are necessary for safe automated decision-making. To develop eXplainable AI (XAI) in these domains the
Externí odkaz:
http://arxiv.org/abs/2406.13584
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
The performance of deep neural network-based speech enhancement systems typically increases with the training dataset size. However, studies that investigated the effect of training dataset size on speech enhancement performance did not consider rece
Externí odkaz:
http://arxiv.org/abs/2406.06160
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
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 diffusion models to other tasks, such as speech enhancement. A popular app
Externí odkaz:
http://arxiv.org/abs/2312.04370
Autor:
Gonzalez, Philippe, Tan, Zheng-Hua, Østergaard, Jan, Jensen, Jesper, Alstrøm, Tommy Sonne, May, Tobias
Diffusion models are a new class of generative models that have recently been applied to speech enhancement successfully. Previous works have demonstrated their superior performance in mismatched conditions compared to state-of-the art discriminative
Externí odkaz:
http://arxiv.org/abs/2312.02683
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 interfering noise, the signal-to-noise ratio (SNR) and the room characteri
Externí odkaz:
http://arxiv.org/abs/2309.06183
Labeling of multivariate biomedical time series data is a laborious and expensive process. Self-supervised contrastive learning alleviates the need for large, labeled datasets through pretraining on unlabeled data. However, for multivariate time seri
Externí odkaz:
http://arxiv.org/abs/2307.09614
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 violate data access rights, and how and when shuffling can accelerate the
Externí odkaz:
http://arxiv.org/abs/2306.13263
Autor:
Tětková, Lenka, Brüsch, Thea, Scheidt, Teresa Karen, Mager, Fabian Martin, Aagaard, Rasmus Ørtoft, Foldager, Jonathan, Alstrøm, Tommy Sonne, Hansen, Lars Kai
Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{\"a}rdenfors' conceptual spaces is a prominent framework for understanding human representations. Convexi
Externí odkaz:
http://arxiv.org/abs/2305.17154