Search Results for author: Felix L. Opolka

Found 8 papers, 3 papers with code

Transductive Kernels for Gaussian Processes on Graphs

no code implementations28 Nov 2022 Yin-Cong Zhi, Felix L. Opolka, Yin Cheng Ng, Pietro Liò, Xiaowen Dong

To address this, we present a novel, generalized kernel for graphs with node feature data for semi-supervised learning.

Gaussian Processes

Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets

no code implementations25 Oct 2021 Felix L. Opolka, Yin-Cong Zhi, Pietro Liò, Xiaowen Dong

Graph-based models require aggregating information in the graph from neighbourhoods of different sizes.

Gaussian Processes

Approximate Latent Force Model Inference

1 code implementation24 Sep 2021 Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu, Pietro Lió

Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems.

Gaussian Processes

Do We Need Anisotropic Graph Neural Networks?

2 code implementations3 Apr 2021 Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane

We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.

Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks

1 code implementation24 Apr 2020 Gevorg Yeghikyan, Felix L. Opolka, Mirco Nanni, Bruno Lepri, Pietro Lio'

A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows.

Social and Information Networks Physics and Society

Spatio-Temporal Deep Graph Infomax

no code implementations12 Apr 2019 Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R. Devon Hjelm

Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time.

Representation Learning Traffic Prediction

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