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07/26/2023
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and...
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02/08/2022
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combin...
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06/16/2020
Understanding and mitigating exploding inverses in invertible neural networks
Invertible neural networks (INNs) have been used to design generative mo...
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02/11/2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Adversarial examples are malicious inputs crafted to induce misclassific...
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12/10/2019
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classificatio...
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06/06/2019
Residual Flows for Invertible Generative Modeling
Flow-based generative models parameterize probability distributions thro...
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11/02/2018
Invertible Residual Networks
Reversible deep networks provide useful theoretical guarantees and have ...
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11/01/2018
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
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06/25/2018
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Studying the invertibility of deep neural networks (DNNs) provides a pri...
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05/02/2017
