Serie: Learning on Graphs Conference 2023

27 nov. 2023

"Learning on Graphs Conference - Keynote Speaker - Xiaowen Dong"

    Vídeo  |  Español  |  76' 35''  |  Visto: 7 veces
Xiaowen Dong
University of Oxford, Xiaowen Dong is an associate professor in the Department of Engineering Science at the University of Oxford, where he is a member of both the Machine Learning Research Group and the Oxford-Man Institute. Prior to joining Oxford, he was a postdoctoral associate in the MIT Media Lab, where he remains as a research affiliate, and received his PhD degree from the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland. His main research interests concern signal processing and machine learning techniques for analysing network data, and their applications in social and economic sciences.

"Learning on Graphs Conference - Selected Talks Monday"

    Vídeo  |  Español  |  108' 39''  |  Visto: 13 veces
Connor Clarkson
Learning Types of Hierarchical Relationships on Complex Class Labelling Systems
Alberto Badias
Structure-Preserving Graph Neural Networks
Christian Koke
ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Samuel Rey
Robust Graph Neural Network based on Graph Denoising
Andrei Buciulea Vlas
Learning Graphs and Simplicial Complexes from Data

28 nov. 2023

"Learning on Graphs Conference - Keynote Speaker -Reinhard Heckel"

    Vídeo  |  Español  |  52' 32''  |  Visto: 2 veces
Reinhard Heckel
Technical University of Munich, Reinhard Heckel is a Rudolf Moessbauer assistant professor in the Department of Computer Engineering at the Technical University of Munich, and an adjunct assistant professor at Rice University, where he was an assistant professor in the ECE department from 2017-2019. Before that, he was a postdoctoral researcher in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and a researcher at the Cognitive Computing & Computational Sciences Department at IBM Research Zurich. He completed his PhD in electrical engineering in 2014 at ETH Zurich and was a visiting PhD student at the Statistics Department at Stanford University. Reinhard is working in the intersection of machine learning and signal/information processing with a current focus on deep networks for solving inverse problems, developing foundations and methods for machine learning, and DNA data storage.

"Learning on Graphs Conference - Selected Talks Tuesday1"

    Vídeo  |  Español  |  87' 15''  |  Visto: 6 veces
Jesús de la Fuente
Towards a more inductive world for drug repurposing approaches
Asieh Abolpour Mofrad
Leveraging Temporal Graph Neural Networks for Drug Repurposing Using Prescription Data in Norway
Óscar Méndez-Lucio
A molecular foundation model for drug discovery based on molecular graphs
Uxía Veleiro
GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks

"Learning on Graphs Conference - Selected Talks Tuesday2"

    Vídeo  |  Español  |  81' 41''  |  Visto: 3 veces
Geert Leus
A General Graph Convolution Theorem: Application to Dual Graph Inference
Iain Rolland
Tensor completion using graph-based diffusion: a remote sensing image completion study
Víctor M Tenorio
Recovering Missing Node Features with Local Structure-based Embeddings
Michelle Wan
Completing Missing Air Quality Data with Graph-Based Techniques

29 nov. 2023

"Learning on Graphs Conference - Keynote Speaker - Ivan Dokmanić"

    Vídeo  |  Español  |  60' 44''  |  Visto: 4 veces
Ivan Dokmanić
Associate Professor at the Department of Mathematics and Computer Science of the University of Basel.

"Learning on Graphs Conference - Selected Talks Wednesday1"

    Vídeo  |  Español  |  66' 23''  |  Visto: 7 veces
Guillermo Megías
Using Graph Neural Networks to Predict Airport Congestion and Air Traffic Delays
Oscar Escudero
Spatio-Temporal Graphs and GNN for Antimicrobial Multidrug Resistance Prediction in Intensive Care Unit
Carlos J. Rodríguez
gMCSpy: Efficient and accurate computation of Genetic Minimal Cut Sets in Python
Oscar Llorente González
Bayesian Graph Neural Networks, how to optimize a cellular network and provide confidence to our customers

"Learning on Graphs Conference - Selected Talks Wednesday2"

    Vídeo  |  Español  |  75' 31''  |  Visto: 10 veces
Manuele Leonelli
Asymmetry-Labeled DAGs: Representation, Learning and Causal Reasoning
Christian Koke
HoloNets: Spectral Convolutions do extend to Directed Graphs
Nathan Mankovich
Graph-Based Dimensionality Reduction and Clustering for Earth and Life Sciences
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