J. Jacobsen

发表

Invertible Residual Networks

David Duvenaud, Jorn-Henrik Jacobsen, Jens Behrmann, 2018, ICML.

i-RevNet: Deep Invertible Networks pdf

Arnold W. M. Smeulders, Jörn-Henrik Jacobsen, Edouard Oyallon, 2018, ICLR.

Excessive Invariance Causes Adversarial Vulnerability pdf

Matthias Bethge, Richard S. Zemel, Jörn-Henrik Jacobsen, 2018, ICLR.

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling pdf

Richard Zemel, David Duvenaud, Jorn-Henrik Jacobsen, 2020, ICML 2020.

Exchanging Lessons Between Algorithmic Fairness and Domain Generalization pdf

Richard Zemel, Jorn-Henrik Jacobsen, Elliot Creager, 2020, ArXiv.

Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness pdf

Jörn-Henrik Jacobsen, Nicholas Carlini, Nicolas Papernot, 2019, ArXiv.

Shortcut Learning in Deep Neural Networks pdf

Matthias Bethge, Felix A. Wichmann, Robert Geirhos, 2020, Nat. Mach. Intell..

How to train your neural ODE pdf

Adam M. Oberman, Jorn-Henrik Jacobsen, Chris Finlay, 2020, ICML.

Using neural networks with jet shapes to identify b jets in e+e− interactions

J. Jacobsen, Y. Pan, S. Wu, 1991 .

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Aaron C. Courville, Rémi Le Priol, J. Jacobsen, 2020, International Conference on Machine Learning.

Invertible Residual Networks

Ricky T. Q. Chen, Jens Behrmann, D. Duvenaud, 2019 .

Selecting Data Augmentation for Simulating Interventions

J. Jacobsen, B. Glocker, David Lopez-Paz, 2022 .

Shortcut learning in deep neural networks

M. Bethge, R. Zemel, J. Jacobsen, 2020, Nature Machine Intelligence.

Bayes-optimal Classification Model Classification Prediction :   Class 0 Attacks Prediction :   Class

J. Jacobsen, 2019 .

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling

R. Zemel, D. Duvenaud, J. Jacobsen, 2020, ICML 2020.

Measurement of αs from the structure of particle clusters produced in hadronic Z decays

P. G. Verdini, J. Jacobsen, J. Hansen, 1991 .

Why musical memory can be preserved in advanced Alzheimer's disease.

J. Jacobsen, R. Turner, G. Chételat, 2015, Brain : a journal of neurology.

On the Invertibility of Invertible Neural Networks

Roger B. Grosse, Jörn-Henrik Jacobsen, Paul Vicol, 2019 .

Measurement of the strong coupling constant αs from global event-shape variables of hadronic Z decays

J. Jacobsen, J. Hansen, G. Apollinari, 1991 .

Learning Invariant Representations with Missing Data

Andrew C. Miller, J. Jacobsen, R. Ranganath, 2021, CLeaR.

Charged particle pair production associated with a lepton pair in Z decays: Indication of an excess in the tau channel

P. G. Verdini, J. Jacobsen, J. Hansen, 1991 .

Measurement of the polarization of tau leptons produced in Z decays

P. G. Verdini, Giorgi, J. Jacobsen, 1991 .

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Aaron C. Courville, Ethan Caballero, Jörn-Henrik Jacobsen, 2020, ICML.

Flexibly Fair Representation Learning by Disentanglement pdf

Toniann Pitassi, Richard S. Zemel, Jörn-Henrik Jacobsen, 2019, ICML.

Residual Flows for Invertible Generative Modeling

David Duvenaud, Jorn-Henrik Jacobsen, Ricky T. Q. Chen, 2019, NeurIPS.

How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization

Adam M. Oberman, Chris Finlay, Jörn-Henrik Jacobsen, 2020, ICML.

Structured convolutional networks

J. H. Jacobsen, J. Jacobsen, 2018 .

Dynamic Steerable Blocks in Deep Residual Networks pdf

Arnold W. M. Smeulders, Jörn-Henrik Jacobsen, Bert De Brabandere, 2017, BMVC.

Dynamic Steerable Frame Networks

Arnold W. M. Smeulders, Jörn-Henrik Jacobsen, Bert De Brabandere, 2016 .

Structured Receptive Fields in CNNs

Arnold W. M. Smeulders, Jörn-Henrik Jacobsen, Jan C. van Gemert, 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Measurement of charge asymmetry in hadronic Z decays

P. G. Verdini, J. Jacobsen, J. Hansen, 1991 .

Measurement of the forward-backward asymmetry in and

T. A. Burnett, P. G. Verdini, J. Jacobsen, 1991 .

Measurement of the B hadron lifetime

P. G. Verdini, J. Jacobsen, J. Hansen, 1991 .

Understanding and mitigating exploding inverses in invertible neural networks

Paul Vicol, Roger Grosse, Kuan-Chieh Wang, 2020, AISTATS.

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling

Richard Zemel, David Duvenaud, Will Grathwohl, 2020, ICML.

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One pdf

Mohammad Norouzi, David Duvenaud, Jorn-Henrik Jacobsen, 2019, ICLR.

Robust Hybrid Learning With Expert Augmentation

Jens Behrmann, J. Jacobsen, G. Sapiro, 2022, Trans. Mach. Learn. Res..

Environment Inference for Invariant Learning

R. Zemel, J. Jacobsen, Elliot Creager, 2020, ICML.

Multiscale Hierarchical Convolutional Networks pdf

Stéphane Mallat, Arnold W. M. Smeulders, Jörn-Henrik Jacobsen, 2017, ArXiv.

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations pdf

Jorn-Henrik Jacobsen, Nicolas Papernot, Nicholas Carlini, 2020, ICML.

Invertible Residual Networks

Jens Behrmann, D. Duvenaud, J. Jacobsen, 2018, ICML.

Simulation-based Inference for Cardiovascular Models

Andrew C. Miller, Jens Behrmann, J. Jacobsen, 2023, ArXiv.

Understanding the Limitations of Conditional Generative Models

R. Zemel, J. Jacobsen, Will Grathwohl, 2019, ICLR.

Dynamic Steerable Blocks in Deep Residual Networks

Bert De Brabandere, J. Jacobsen, A. Smeulders, 2017, BMVC.

Why are NLP Models Fumbling at Elementary Math? A Survey of Automatic Word Problem Solvers

R. Socher, Kyung Seo Ki, J. Jacobsen, 2022 .

Unintended cue learning: Lessons for deep learning from experimental psychology

M. Bethge, R. Zemel, J. Jacobsen, 2020 .

Conditional Generative Models are not Robust

Richard Zemel, Jorn-Henrik Jacobsen, Ethan Fetaya, 2019, ArXiv.