
Exchanging Lessons Between Algorithmic Fairness and Domain Generalization
Standard learning approaches are designed to perform well on average for...
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Understanding and mitigating exploding inverses in invertible neural networks
Invertible neural networks (INNs) have been used to design generative mo...
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Shortcut Learning in Deep Neural Networks
Deep learning has triggered the current rise of artificial intelligence ...
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Cutting out the MiddleMan: Training and Evaluating EnergyBased Models without Sampling
We present a new method for evaluating and training unnormalized density...
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Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Adversarial examples are malicious inputs crafted to induce misclassific...
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How to train your neural ODE
Training neural ODEs on large datasets has not been tractable due to the...
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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(yx)...
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Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks
Lipschitz constraints under L2 norm on deep neural networks are useful f...
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Residual Flows for Invertible Generative Modeling
Flowbased generative models parameterize probability distributions thro...
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Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group a...
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Conditional Generative Models are not Robust
Classconditional generative models are an increasingly popular approach...
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Exploiting Excessive Invariance caused by NormBounded Adversarial Robustness
Adversarial examples are malicious inputs crafted to cause a model to mi...
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Invertible Residual Networks
Reversible deep networks provide useful theoretical guarantees and have ...
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Excessive Invariance Causes Adversarial Vulnerability
Despite their impressive performance, deep neural networks exhibit strik...
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iRevNet: Deep Invertible Networks
It is widely believed that the success of deep convolutional networks is...
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Dynamic Steerable Blocks in Deep Residual Networks
Filters in convolutional networks are typically parameterized in a pixel...
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Multiscale Hierarchical Convolutional Networks
Deep neural network algorithms are difficult to analyze because they lac...
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JörnHenrik Jacobsen
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