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Found 8 papers, 0 papers with code
Persistent Classification: A New Approach to Stability of Data and Adversarial Examples
This article proposes a new framework for studying adversarial examples that does not depend directly on the distance to the decision boundary.
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private training data just by repeatedly querying the network and inspecting its outputs.
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
Deep neural networks (DNNs) are easily fooled by adversarial perturbations that are imperceptible to humans.
An Exact Kernel Equivalence for Finite Classification Models
We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine.
Laplacian Segmentation Networks: Improved Epistemic Uncertainty from Spatial Aleatoric Uncertainty
To ensure robustness to such incorrect segmentations, we propose Laplacian Segmentation Networks (LSN) that jointly model epistemic (model) and aleatoric (data) uncertainty in image segmentation.
Is the Discrete VAE’s Power Stuck in its Prior?
We investigate why probabilistic neural models with discrete latent variables are effective at generating high-quality images.
Inferring Multilateral Relations from Dynamic Pairwise Interactions
Correlations between anomalous activity patterns can yield pertinent information about complex social processes: a significant deviation from normal behavior, exhibited simultaneously by multiple pairs of actors, provides evidence for some underlying relationship involving those pairs---i. e., a multilateral relation.
Topic-Partitioned Multinetwork Embeddings
We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns.
