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Found 10 papers, 2 papers with code
Ising on the Graph: Task-specific Graph Subsampling via the Ising Model
Typically, the reduction approaches either remove edges (sparsification) or merge nodes (coarsening) in an unsupervised way with no specific downstream task in mind.
Provably-Correct Safety Protocol for Cooperative Platooning
Cooperative Adaptive Cruise Control (CACC) is a well-studied technology for forming string-stable vehicle platoons.
On Feynman--Kac training of partial Bayesian neural networks
Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural networks.
Personalized Privacy Amplification via Importance Sampling
We first consider the general case where an arbitrary personalized differentially private mechanism is subsampled with an arbitrary importance sampling distribution and show that the resulting mechanism also satisfies personalized differential privacy.
Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-organization Strategies and Provably-correct Design
We show that self-organization promotes resilience to acceleration limits and communication failures, i. e., homogenizing to a common group behavior makes the platoon recover from these causes of impairments.
Self-Supervised Siamese Autoencoders
In this paper, we present a new self-supervised method that combines the benefits of Siamese architectures and denoising autoencoders.
Archetypal Analysis++: Rethinking the Initialization Strategy
Archetypal analysis is a matrix factorization method with convexity constraints.
Principled Interpolation in Normalizing Flows
In this paper, we solve this issue by enforcing a fixed norm and, hence, change the base distribution, to allow for a principled way of interpolation.
Coresets for Archetypal Analysis
Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data.
Frame-based Data Factorizations
Archetypal Analysis is the method of choice to compute interpretable matrix factorizations.
