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Found 2 papers, 1 papers with code
Scalable Diverse Model Selection for Accessible Transfer Learning
In this paper, we formalize this setting as "Scalable Diverse Model Selection" and propose several benchmarks for evaluating on this task.
Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses
Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally occurring and of low probability.
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