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cs.LG2026
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
Magdalena Proszewska, N. Siddharth
Graph Neural Networks (GNNs) have advanced significantly in handling graph-structured data, but a comprehensive framework for evaluating explainability remains lacking. Existing ev…
cs.LG2025
On Designing Diffusion Autoencoders for Efficient Generation and Representation Learning
Magdalena Proszewska, Nikolay Malkin, N. Siddharth
Diffusion autoencoders (DAs) are variants of diffusion generative models that use an input-dependent latent variable to capture representations alongside the diffusion process. The…
cs.LG2024
Learning High-Frequency Functions Made Easy with Sinusoidal Positional Encoding
Chuanhao Sun, Zhihang Yuan, Kai Xu +4
Fourier features based positional encoding (PE) is commonly used in machine learning tasks that involve learning high-frequency features from low-dimensional inputs, such as 3D vie…