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Found 13 papers, 5 papers with code
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico.
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data.
Generalization of the Change of Variables Formula with Applications to Residual Flows
Normalizing flows leverage the Change of Variables Formula (CVF) to define flexible density models.
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
For problems where global invertibility is necessary, such as applying normalizing flows on OOD data, we show the importance of designing stable INN building blocks.
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Adversarial examples are malicious inputs crafted to induce misclassification.
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing.
On the Invertibility of Invertible Neural Networks
Guarantees in deep learning are hard to achieve due to the interplay of flexible modeling schemes and complex tasks.
Residual Flows for Invertible Generative Modeling
Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood.
Ranked #2 on
Image Generation
on MNIST
Invariance and Inverse Stability under ReLU
We flip the usual approach to study invariance and robustness of neural networks by considering the non-uniqueness and instability of the inverse mapping.
Invertible Residual Networks
We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation.
Ranked #5 on
Image Generation
on MNIST
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs.
Analysis of Invariance and Robustness via Invertibility of ReLU-Networks
Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models.
Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Deep learning offers an approach to learn feature extraction and classification combined in a single model.
