4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2025
LEAP: Local ECT-Based Learnable Positional Encodings for Graphs
Juan Amboage, Ernst Röell, Patrick Schnider +1
Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet, standard message passing neural…
cs.CV2024
Point Cloud Synthesis Using Inner Product Transforms
Ernst Röell, Bastian Rieck
Point cloud synthesis, i.e. the generation of novel point clouds from an input distribution, remains a challenging task, for which numerous complex machine learning models have bee…
cs.CV2022★ 4 cited
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Dominik J. E. Waibel, Ernst Röell, Bastian Rieck +2
Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the in…