2.1k citations · 3.3k across the 23 of their papers we have counts for
25 papers
Inferring Optical Tissue Properties from Photoplethysmography using Hybrid Amortized Inference
Jens Behrmann, Maria R. Cervera, Antoine Wehenkel +8
Smart wearables enable continuous tracking of established biomarkers such as heart rate, heart rate variability, and blood oxygen saturation via photoplethysmography (PPG). Beyond…
Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers
Laura Manduchi, Antoine Wehenkel, Jens Behrmann +6
Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular syst…
Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcare
Arno Blaas, Adam Goliński, Andrew Miller +3
We consider robustness to distribution shifts in the context of diagnostic models in healthcare, where the prediction target , e.g., the presence of a disease, is causally upstr…
Addressing Misspecification in Simulation-based Inference through Data-driven Calibration
Antoine Wehenkel, Juan L. Gamella, Ozan Sener +4
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,…
Simulation-based Inference for Cardiovascular Models
Antoine Wehenkel, Laura Manduchi, Jens Behrmann +6
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. While such tools are routinely u…
Robust Hybrid Learning With Expert Augmentation
Antoine Wehenkel, Jens Behrmann, Hsiang Hsu +3
Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid mo…
Learning Invariant Representations with Missing Data
Mark Goldstein, Jörn-Henrik Jacobsen, Olina Chau +4
Spurious correlations allow flexible models to predict well during training but poorly on related test distributions. Recent work has shown that models that satisfy particular inde…
Environment Inference for Invariant Learning
Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel
Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-inv…
Understanding and Mitigating Exploding Inverses in Invertible Neural Networks
Jens Behrmann, Paul Vicol, Kuan-Chieh Wang +2
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 c…
Shortcut Learning in Deep Neural Networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis +4
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today's machine intelligence. Numerous success stories have rapidly spread all over…
Out-of-Distribution Generalization via Risk Extrapolation (REx)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen +5
Distributional shift is one of the major obstacles when transferring machine learning prediction systems from the lab to the real world. To tackle this problem, we assume that vari…
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling
Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen +2
We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model's log-density. We estim…
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Florian Tramèr, Jens Behrmann, Nicholas Carlini +2
Adversarial examples are malicious inputs crafted to induce misclassification. Commonly studied sensitivity-based adversarial examples introduce semantically-small changes to an in…
How to train your neural ODE: the world of Jacobian and kinetic regularization
Chris Finlay, Jörn-Henrik Jacobsen, Levon Nurbekyan +1
Training neural ODEs on large datasets has not been tractable due to the necessity of allowing the adaptive numerical ODE solver to refine its step size to very small values. In pr…
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen +3
We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabiliti…
Flexibly Fair Representation Learning by Disentanglement
Elliot Creager, David Madras, Jörn-Henrik Jacobsen +4
We consider the problem of learning representations that achieve group and subgroup fairness with respect to multiple sensitive attributes. Taking inspiration from the disentangled…
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud +1
Flow-based generative models parameterize probability distributions through an invertible transformation and can be trained by maximum likelihood. Invertible residual networks prov…
Understanding the Limitations of Conditional Generative Models
Ethan Fetaya, Jörn-Henrik Jacobsen, Will Grathwohl +1
Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a rob…
Exploiting Excessive Invariance caused by Norm-Bounded Adversarial Robustness
Jörn-Henrik Jacobsen, Jens Behrmannn, Nicholas Carlini +2
Adversarial examples are malicious inputs crafted to cause a model to misclassify them. Their most common instantiation, "perturbation-based" adversarial examples introduce changes…
Invertible Residual Networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen +2
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 i…
Excessive Invariance Causes Adversarial Vulnerability
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel +1
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 neura…
i-RevNet: Deep Invertible Networks
Jörn-Henrik Jacobsen, Arnold Smeulders, Edouard Oyallon
It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at…
Dynamic Steerable Blocks in Deep Residual Networks
Jörn-Henrik Jacobsen, Bert de Brabandere, Arnold W. M. Smeulders
Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generaliz…
Multiscale Hierarchical Convolutional Networks
Jörn-Henrik Jacobsen, Edouard Oyallon, Stéphane Mallat +1
Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierar…