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Found 7 papers, 3 papers with code
Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels
We therefore propose the "local signal adaptivity" (LSA) phenomenon as one explanation for the superiority of neural networks over kernel methods.
Monotone deep Boltzmann machines
In this paper, we propose a class of model that allows for \emph{exact, efficient} mean-field inference and learning in \emph{general} deep Boltzmann machines.
Estimating Lipschitz constants of monotone deep equilibrium models
Several methods have been proposed in recent years to provide bounds on the Lipschitz constants of deep networks, which can be used to provide robustness guarantees, generalization bounds, and characterize the smoothness of decision boundaries.
Monotone operator equilibrium networks
We then develop a parameterization of the network which ensures that all operators remain monotone, which guarantees the existence of a unique equilibrium point.
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Machine learning algorithms are known to be susceptible to data poisoning attacks, where an adversary manipulates the training data to degrade performance of the resulting classifier.
Certified Robustness to Adversarial Label-Flipping Attacks via Randomized Smoothing
This paper considers label-flipping attacks, a type of data poisoning attack where an adversary relabels a small number of examples in a training set in order to degrade the performance of the resulting classifier.
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Domain adaptation addresses the common problem when the target distribution generating our test data drifts from the source (training) distribution.
