Machine Learning for Informed Representation Learning. M. Samarin.
PhD Thesis, 2022.
[PDF]
Mesh-free Eulerian Physics-Informed Neural Networks.
F. Arend Torres, M. Negri, M. Nagy-Huber, M. Samarin & V. Roth.
arXiv preprint arXiv:2206.01545, 2022.
[arXiv]
Feature Learning and Random Features in Standard Finite-Width Convolutional Neural Networks: An Empirical Study. M. Samarin, V. Roth & D. Belius.
Accepted to the Conference on Uncertainty in Artificial Intelligence (UAI) 2022.
[paper, OpenReview]
Learning Invariances with Generalised Input-Convex Neural Networks.
V. Nesterov, F. Arend Torres, M. Nagy-Huber, M. Samarin & V. Roth.
arXiv preprint arXiv:2204.07009, 2022.
[arXiv]
Learning Conditional Invariance through Cycle Consistency. M. Samarin*, V. Nesterov*, M. Wieser, A. Wieczorek, S. Parbhoo, & V. Roth.
Conference on Pattern Recognition (GCPR), 2021.
[video, paper, arXiv, code]
Investigating Causal Factors of Shallow Landslides in Grassland Regions of Switzerland.
L. Zweifel, M. Samarin, K. Meusburger, & C. Alewell.
Natural Hazards and Earth System Sciences (NHESS), 2021.
[paper]
Learning Extremal Representations with Deep Archetypal Analysis.
S. M. Keller, M. Samarin, F. Arend Torres, M. Wieser & V. Roth.
International Journal on Computer Vision (IJCV), 2020.
[paper, arXiv, code]
Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network. M. Samarin*, L. Zweifel*, V. Roth & C. Alewell. Remote Sensing, 2020.
[paper, code]
On the Empirical Neural Tangent Kernel of Standard Finite-Width Convolutional Neural Network Architectures. M. Samarin, V. Roth & D. Belius.
arXiv preprint arXiv:2006.13645, 2020.
[arXiv]
