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Found 15 papers, 5 papers with code
A Characterization Theorem for Equivariant Networks with Point-wise Activations
Equivariant neural networks have shown improved performance, expressiveness and sample complexity on symmetrical domains.
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation
To gauge the practicality and acceptability of the policy, we conduct evaluations with human participants using the simulator, focusing on a range of metrics like traffic smoothness and safety perception.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
Interpolation with the polynomial kernels
We will then study the Reproducing Kernel Hilbert Spaces (or native spaces) of these kernels and their norms, and provide inclusion relations between spaces corresponding to different kernel parameters.
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.
A Framework for Verifiable and Auditable Federated Anomaly Detection
Federated Leaning is an emerging approach to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data.
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment.
Universality and Optimality of Structured Deep Kernel Networks
In particular, we show that the use of special types of kernels yield models reminiscent of neural networks that are founded in the same theoretical framework of classical kernel methods, while enjoying many computational properties of deep neural networks.
Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows
Standard kernel methods for machine learning usually struggle when dealing with large datasets.
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization
The inference of novel knowledge, the discovery of hidden patterns, and the uncovering of insights from large amounts of data from a multitude of sources make Data Science (DS) to an art rather than just a mere scientific discipline.
Kernel methods for center manifold approximation and a data-based version of the Center Manifold Theorem
We then use an apposite data-based kernel method to construct a suitable approximation of the manifold close to the equilibrium, which is compatible with our general error theory.
Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods
Greedy kernel approximation algorithms are successful techniques for sparse and accurate data-based modelling and function approximation.
A novel class of stabilized greedy kernel approximation algorithms: Convergence, stability & uniform point distribution
Since the computation of an optimal selection of sampling points may be an infeasible task, one promising option is to use greedy methods.
Numerical Analysis Numerical Analysis
Kernel Methods for Surrogate Modeling
Second, if a function is available only via measurements or a few function evaluation samples, kernel approximation techniques can provide function surrogates that allow global evaluation.
Numerical Analysis Numerical Analysis
Greedy regularized kernel interpolation
Kernel based regularized interpolation is a well known technique to approximate a continuous multivariate function using a set of scattered data points and the corresponding function evaluations, or data values.
Numerical Analysis
