Search Results for author:
Found 18 papers, 10 papers with code
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
We assess existing state-of-the-art planners on our benchmark and show that neither rule-based nor learning-based planners can safely navigate the interPlan scenarios.
A Preconditioned Interior Point Method for Support Vector Machines Using an ANOVA-Decomposition and NFFT-Based Matrix-Vector Products
In this paper we consider the numerical solution to the soft-margin support vector machine optimization problem.
Rethinking Integration of Prediction and Planning in Deep Learning-Based Automated Driving Systems: A Review
We systematically review state-of-the-art deep learning-based prediction, planning, and integrated prediction and planning models.
A weighted subspace exponential kernel for support tensor machines
High-dimensional data in the form of tensors are challenging for kernel classification methods.
Gibbs-Helmholtz Graph Neural Network: capturing the temperature dependency of activity coefficients at infinite dilution
The accurate prediction of physicochemical properties of chemical compounds in mixtures (such as the activity coefficient at infinite dilution $\gamma_{ij}^\infty$) is essential for developing novel and more sustainable chemical processes.
A comparison of PINN approaches for drift-diffusion equations on metric graphs
In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric graphs, i. e., graphs with dedicated edge lengths, and an associated differential operator.
Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication
We propose the use of an ANOVA kernel, where we construct several kernels based on lower-dimensional feature spaces for which we provide fast algorithms realizing the matrix-vector products.
RaWaNet: Enriching Graph Neural Network Input via Random Walks on Graphs
In recent years, graph neural networks (GNNs) have gained increasing popularity and have shown very promising results for data that are represented by graphs.
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification
Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data.
Pseudoinverse Graph Convolutional Networks: Fast Filters Tailored for Large Eigengaps of Dense Graphs and Hypergraphs
Our method overcomes these issues by utilizing the pseudoinverse of the Laplacian.
Semi-supervised Learning for Aggregated Multilayer Graphs Using Diffuse Interface Methods and Fast Matrix Vector Products
We generalize a graph-based multiclass semi-supervised classification technique based on diffuse interface methods to multilayer graphs.
Efficient Structure-preserving Support Tensor Train Machine
An increasing amount of collected data are high-dimensional multi-way arrays (tensors), and it is crucial for efficient learning algorithms to exploit this tensorial structure as much as possible.
A literature survey of matrix methods for data science
Efficient numerical linear algebra is a core ingredient in many applications across almost all scientific and industrial disciplines.
Improved penalty algorithm for Mixed Integer PDE Constrained Optimization Problems
Optimal control problems including partial differential equation (PDE) as well as integer constraints merge the combinatorial difficulties of integer programming and the challenges related to large-scale systems resulting from discretized PDEs.
Numerical Analysis Numerical Analysis Optimization and Control 65K05, 90C06, 90C11, 93C20, 90C51
Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets.
Node Classification for Signed Social Networks Using Diffuse Interface Methods
Signed networks contain both positive and negative kinds of interactions like friendship and enmity.
NFFT meets Krylov methods: Fast matrix-vector products for the graph Laplacian of fully connected networks
The graph Laplacian is a standard tool in data science, machine learning, and image processing.
Generalizing diffuse interface methods on graphs: non-smooth potentials and hypergraphs
Additionally, we show that the diffuse interface method can be used for the segmentation of data coming from hypergraphs.
