6 citations · 7 across the 2 of their papers we have counts for
8 papers
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Vignesh Srinivasan, Nils Strodthoff, Jackie Ma +3
There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical ex…
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models
Nils Strodthoff, Claas Strodthoff, Tobias Becher +2
Electrical impedance tomography (EIT) is a noninvasive imaging modality that allows a continuous assessment of changes in regional bioimpedance of different organs. One of its most…
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Nils Strodthoff, Patrick Wagner, Tobias Schaeffter +1
Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. The progress in the…
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff +3
We propose a general framework for the estimation of observables with generative neural samplers focusing on modern deep generative neural networks that provide an exact sampling p…
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
Jan Laermann, Wojciech Samek, Nils Strodthoff
We study the recently introduced stability training as a general-purpose method to increase the robustness of deep neural networks against input perturbations. In particular, we ex…
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
Kim Nicoli, Pan Kessel, Nils Strodthoff +3
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach. We show…