Jörn-Henrik Jacobsen | DeepAI

DeepAI AI Chat

Featured Co-authors

Nicolas Papernot
83 publications
Guillermo Sapiro
75 publications
Mohammad Norouzi
69 publications
Matthias Bethge
61 publications
Richard Zemel
58 publications
Nicholas Carlini
56 publications
Roger Grosse
55 publications
Rajesh Ranganath
54 publications
David Duvenaud
48 publications
Gilles Louppe
47 publications
Kevin Swersky
39 publications

∙ 02/08/2022

Robust Hybrid Learning With Expert Augmentation

Hybrid modelling reduces the misspecification of expert models by combin...

14 Antoine Wehenkel, et al. ∙

∙ 12/01/2021

Learning Invariant Representations with Missing Data

Spurious correlations allow flexible models to predict well during train...

0 Mark Goldstein, et al. ∙

∙ 10/14/2020

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization

Standard learning approaches are designed to perform well on average for...

0 Elliot Creager, et al. ∙

∙ 06/16/2020

Understanding and mitigating exploding inverses in invertible neural networks

Invertible neural networks (INNs) have been used to design generative mo...

14 Jens Behrmann, et al. ∙

∙ 04/16/2020

Shortcut Learning in Deep Neural Networks

Deep learning has triggered the current rise of artificial intelligence ...

3 Robert Geirhos, et al. ∙

∙ 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...

1 Will Grathwohl, et al. ∙

∙ 02/11/2020

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Adversarial examples are malicious inputs crafted to induce misclassific...

0 Florian Tramèr, et al. ∙

∙ 02/07/2020

How to train your neural ODE

Training neural ODEs on large datasets has not been tractable due to the...

0 Chris Finlay, et al. ∙

∙ 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)...

19 Will Grathwohl, et al. ∙

∙ 11/03/2019

Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks

Lipschitz constraints under L2 norm on deep neural networks are useful f...

4 Qiyang Li, et al. ∙

∙ 06/06/2019

Residual Flows for Invertible Generative Modeling

Flow-based generative models parameterize probability distributions thro...

2 Ricky T. Q. Chen, et al. ∙

∙ 06/06/2019

Flexibly Fair Representation Learning by Disentanglement

We consider the problem of learning representations that achieve group a...

4 Elliot Creager, et al. ∙

∙ 06/04/2019

Conditional Generative Models are not Robust

Class-conditional generative models are an increasingly popular approach...

0 Ethan Fetaya, et al. ∙

∙ 03/25/2019

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness

Adversarial examples are malicious inputs crafted to cause a model to mi...

6 Jörn-Henrik Jacobsen, et al. ∙

∙ 11/02/2018

Invertible Residual Networks

Reversible deep networks provide useful theoretical guarantees and have ...

16 Jens Behrmann, et al. ∙

∙ 11/01/2018

Excessive Invariance Causes Adversarial Vulnerability

Despite their impressive performance, deep neural networks exhibit strik...

16 Jörn-Henrik Jacobsen, et al. ∙

∙ 02/20/2018

i-RevNet: Deep Invertible Networks

It is widely believed that the success of deep convolutional networks is...

0 Jörn-Henrik Jacobsen, et al. ∙

∙ 06/02/2017

Dynamic Steerable Blocks in Deep Residual Networks

Filters in convolutional networks are typically parameterized in a pixel...

0 Jörn-Henrik Jacobsen, et al. ∙

∙ 03/12/2017

Multiscale Hierarchical Convolutional Networks

Deep neural network algorithms are difficult to analyze because they lac...

0 Jörn-Henrik Jacobsen, et al. ∙