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Jens Behrmann

6 accepted papers

2025

Addressing Misspecification in Simulation-based Inference through Data-driven Calibration

Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Joern-Henrik Jacobsen +1
ICML 2025oral

Driven by steady progress in deep generative modeling, simulation-based inference (SBI) has emerged as the workhorse for inferring the parameters of stochastic simulators. However, recent work has demonstrated that model misspecification can harm SBI's reliability, preventing its adoption in importa…

Cited by 12Source ↗PDF ↗Scholar ↗
2021

Understanding and Mitigating Exploding Inverses in Invertible Neural Networks

Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, Joern-Henrik Jacobsen
AISTATS 2021poster

Invertible neural networks (INNs) have been used to design generative models, implement memory-saving gradient computation, and solve inverse problems. In this work, we show that commonly-used INN architectures suffer from exploding inverses and are thus prone to becoming numerically non-invertible.…

Cited by 122Source ↗PDF ↗code ↗Scholar ↗
2020

Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations

Florian Tramer, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Joern-Henrik Jacobsen
ICML 2020poster

Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied \emph{sensitivity-based} adversarial examples introduce semantically-small changes to an input that result in a different model prediction. This paper studies a complementary failure mode, \emph{invarianc…

Cited by 111Source ↗PDF ↗code ↗Scholar ↗
2019

Excessive Invariance Causes Adversarial Vulnerability

Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
ICLR 2019poster

Despite their impressive performance, deep neural networks exhibit striking failures on out-of-distribution inputs. One core idea of adversarial example research is to reveal neural network errors under such distribution shifts. We decompose these errors into two complementary sources: sensitivity a…

Cited by 201Source ↗PDF ↗Scholar ↗
2019

Invertible Residual Networks

Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Joern-Henrik Jacobsen
ICML 2019oral

We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Typically, enforcing invertibility requires partitioning dimensions or restricting network architectures. In contrast, our approach only requi…

Cited by 759Source ↗PDF ↗Scholar ↗
2019

Residual Flows for Invertible Generative Modeling

Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud, Joern-Henrik Jacobsen
NeurIPS 2019spotlight

Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks provide a flexible family of transformations where only Lipschitz conditions rather than strict architectural constraints ar…

Cited by 451Source ↗PDF ↗code ↗Scholar ↗