research
∙
07/26/2023
Simulation-based Inference for Cardiovascular Models
Over the past decades, hemodynamics simulators have steadily evolved and...
share
research
∙
02/08/2022
Robust Hybrid Learning With Expert Augmentation
Hybrid modelling reduces the misspecification of expert models by combin...
research
∙
12/01/2021
Learning Invariant Representations with Missing Data
Spurious correlations allow flexible models to predict well during train...
research
∙
10/14/2020
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
Standard learning approaches are designed to perform well on average for...
research
∙
06/16/2020
Understanding and mitigating exploding inverses in invertible neural networks
Invertible neural networks (INNs) have been used to design generative mo...
research
∙
04/16/2020
Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
research
∙
02/13/2020
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling
We present a new method for evaluating and training unnormalized density...
research
∙
02/11/2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Adversarial examples are malicious inputs crafted to induce misclassific...
research
∙
02/07/2020
How to train your neural ODE
Training neural ODEs on large datasets has not been tractable due to the...
research
∙
12/06/2019
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
We propose to reinterpret a standard discriminative classifier of p(y|x)...
research
∙
11/03/2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Lipschitz constraints under L2 norm on deep neural networks are useful f...
research
∙
06/06/2019
Residual Flows for Invertible Generative Modeling
Flow-based generative models parameterize probability distributions thro...
research
∙
06/06/2019
Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group a...
research
∙
06/04/2019
Conditional Generative Models are not Robust
Class-conditional generative models are an increasingly popular approach...
research
∙
03/25/2019
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Adversarial examples are malicious inputs crafted to cause a model to mi...
research
∙
11/02/2018
Invertible Residual Networks
Reversible deep networks provide useful theoretical guarantees and have ...
research
∙
11/01/2018
Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
research
∙
02/20/2018
i-RevNet: Deep Invertible Networks
It is widely believed that the success of deep convolutional networks is...
research
∙
06/02/2017
Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel...
research
∙
03/12/2017