2.1k citations · 3.3k across the 23 of their papers we have counts for
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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…