Thomas Kipf

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Max WellingDistinguished Scientist at MSR AI4Science & Professor Machine Learning, University of AmsterdamVerified email at uva.nl
Rianne van den BergPrincipal Research Manager, Microsoft ResearchVerified email at microsoft.com
Aravindh MahendranGoogle DeepmindVerified email at google.com
Nicola De CaoResearch Scientist at Google DeepMindVerified email at google.com
Klaus GreffResearch Scientist at Google BrainVerified email at usi.ch
Ivan TitovUniversity of Edinburgh / University of AmsterdamVerified email at inf.ed.ac.uk
Alexey DosovitskiyInceptiveVerified email at inceptive.team
Peter BloemVrije Universiteit AmsterdamVerified email at peterbloem.nl
Michael Sejr SchlichtkrullLecturer, Queen Mary University of LondonVerified email at qmul.ac.uk
Georg HeigoldResearch Scientist, Google Inc.Verified email at google.com
Sjoerd van SteenkisteResearch Scientist at Google ResearchVerified email at google.com
Dirk WeissenbornInceptive Inc.Verified email at inceptive.team
Jakob UszkoreitInceptiveVerified email at uszkoreit.net
Gamaleldin ElsayedResearch Scientist, Google DeepMindVerified email at google.com
Mehdi S. M. SajjadiResearch Scientist, Google DeepMindVerified email at google.com
Petar VeličkovićStaff Research Scientist, Google DeepMind | Affiliated Lecturer, University of CambridgeVerified email at google.com
Jakub M. TomczakAssociate professor at Eindhoven University of TechnologyVerified email at tue.nl
Francesco LocatelloAssistant Professor, ISTAVerified email at ista.ac.at
Thomas UnterthinerGoogle DeepMindVerified email at pm.me
Elise van der PolMicrosoft ResearchVerified email at microsoft.com

Thomas Kipf

Other namesThomas N. Kipf

Staff Research Scientist, Google DeepMind

Verified email at google.com - Homepage

Machine Learning Graph Representation Learning Graph Neural Networks


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Semi-supervised classification with graph convolutional networks TN Kipf, M Welling International Conference on Learning Representations (ICLR), 2016	35391	2016
Modeling Relational Data with Graph Convolutional Networks M Schlichtkrull, TN Kipf, P Bloem, R Berg, I Titov, M Welling Extended Semantic Web Conference (ESWC), 2017	5166	2017
Variational Graph Auto-Encoders TN Kipf, M Welling NeurIPS Workshop on Bayesian Deep Learning (NeurIPS BDL), 2016	2911	2016
MolGAN: An implicit generative model for small molecular graphs N De Cao, T Kipf ICML Workshop on Theoretical Foundations and Applications of Deep …, 2018	1073	2018
Neural Relational Inference for Interacting Systems T Kipf, E Fetaya, KC Wang, M Welling, R Zemel International Conference on Machine Learning (ICML), 2018	958	2018
Graph convolutional matrix completion R Van Den Berg, NK Thomas, M Welling arXiv preprint arXiv:1706.02263 2 (8), 9, 2017	873	2017
Graph convolutional matrix completion R Berg, TN Kipf, M Welling KDD Deep Learning Day (KDD DLD), 2017	780	2017
Object-Centric Learning with Slot Attention F Locatello*, D Weissenborn, T Unterthiner, A Mahendran, G Heigold, ... Advances in Neural Information Processing Systems (NeurIPS), 2020	687	2020
Hyperspherical Variational Auto-Encoders TR Davidson, L Falorsi, N De Cao*, T Kipf, JM Tomczak Conference on Uncertainty in Artificial Intelligence (UAI), 2018	455	2018
Learned cardinalities: Estimating correlated joins with deep learning A Kipf, T Kipf, B Radke, V Leis, P Boncz, A Kemper Conference on Innovative Data Systems Research (CIDR), 2018	433	2018
Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning (ICML), 2023	331	2023
Simple open-vocabulary object detection with vision transformers M Minderer, A Gritsenko, A Stone, M Neumann, D Weissenborn, ... European Conference on Computer Vision (ECCV), 2022	313*	2022
Contrastive learning of structured world models T Kipf, E Van der Pol, M Welling International Conference on Learning Representations (ICLR), 2019	296	2019
Towards Sparse Hierarchical Graph Classifiers C Cangea, P Veličković, N Jovanović, T Kipf, P Liò NeurIPS Workshop on Relational Representation Learning (NeurIPS R2L), 2018	269	2018
Conditional object-centric learning from video T Kipf, GF Elsayed, A Mahendran, A Stone, S Sabour, G Heigold, ... International Conference on Learning Representations (ICLR), 2021	169	2021
Kubric: A scalable dataset generator K Greff, F Belletti, L Beyer, C Doersch, Y Du, D Duckworth, DJ Fleet, ... Conference on Computer Vision and Pattern Recognition (CVPR), 3749-3761, 2022	150	2022
CompILE: Compositional imitation learning and execution T Kipf, Y Li, H Dai, V Zambaldi, A Sanchez-Gonzalez, E Grefenstette, ... International Conference on Machine Learning (ICML), 2019	124	2019
SAVi++: Towards end-to-end object-centric learning from real-world videos GF Elsayed, A Mahendran, S van Steenkiste*, K Greff, MC Mozer, ... Advances in Neural Information Processing Systems (NeurIPS), 2022	102	2022
Plannable approximations to mdp homomorphisms: Equivariance under actions E van der Pol, T Kipf, FA Oliehoek, M Welling International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2020	86	2020
Object Scene Representation Transformer MSM Sajjadi, D Duckworth, A Mahendran, S van Steenkiste, F Pavetić, ... Advances in Neural Information Processing Systems (NeurIPS), 2022	83	2022

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