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09/07/2023
Insights Into the Inner Workings of Transformer Models for Protein Function Prediction
Motivation: We explored how explainable AI (XAI) can help to shed light ...
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08/29/2023
Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata
Deep learning has emerged as the preferred modeling approach for automat...
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05/26/2023
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
Deep neural networks have become increasingly popular for analyzing ECG ...
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04/05/2023
ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Feature importance methods promise to provide a ranking of features acco...
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01/27/2023
Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
The completeness axiom renders the explanation of a post-hoc XAI method ...
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01/19/2023
Diffusion-based Conditional ECG Generation with Structured State Space Models
Synthetic data generation is a promising solution to address privacy iss...
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11/14/2022
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
The field of deep-learning-based ECG analysis has been largely dominated...
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08/19/2022
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
The imputation of missing values represents a significant obstacle for m...
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03/11/2022
Sparse Subspace Clustering for Concept Discovery (SSCCD)
Concepts are key building blocks of higher level human understanding. Ex...
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06/25/2021
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
There is an increasing number of medical use-cases where classification ...
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04/16/2021
Predicting the Binding of SARS-CoV-2 Peptides to the Major Histocompatibility Complex with Recurrent Neural Networks
Predicting the binding of viral peptides to the major histocompatibility...
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03/23/2021
Self-supervised representation learning from 12-lead ECG data
We put forward a comprehensive assessment of self-supervised representat...
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02/26/2021
PredDiff: Explanations and Interactions from Conditional Expectations
PredDiff is a model-agnostic, local attribution method that is firmly ro...
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12/18/2020
Generative Neural Samplers for the Quantum Heisenberg Chain
Generative neural samplers offer a complementary approach to Monte Carlo...
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10/19/2020
Inferring respiratory and circulatory parameters from electrical impedance tomography with deep recurrent models
Electrical impedance tomography (EIT) is a noninvasive imaging modality ...
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04/28/2020
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL
Electrocardiography is a very common, non-invasive diagnostic procedure ...
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03/03/2020
Towards Novel Insights in Lattice Field Theory with Explainable Machine Learning
Machine learning has the potential to aid our understanding of phase str...
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10/29/2019
Asymptotically Unbiased Generative Neural Sampling
We propose a general framework for the estimation of observables with ge...
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06/03/2019
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
We study the recently introduced stability training as a general-purpose...
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03/26/2019
Comment on "Solving Statistical Mechanics Using VANs": Introducing saVANt - VANs Enhanced by Importance and MCMC Sampling
In this comment on "Solving Statistical Mechanics Using Variational Auto...
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07/27/2018
Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G
We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback s...
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06/18/2018
