Thomas Kipf

PhD candidate

University of Amsterdam

I am a third-year PhD student in Machine Learning at the University of Amsterdam, advised by Prof. Max Welling. My main area of interest is learning with structured data and structured representations/computations. This includes topics in reasoning, (multi-agent) reinforcement learning, and structured deep generative models.

My formal background is in Physics (M.Sc. hons. 2016, B.Sc. 2014 at FAU). During my studies, I have had exposure to a number of fields and—after a short interlude in Neuroscience-related research at the Max Planck Institute for Brain Research—eventually found my way into Machine Learning.

News

Recent Publications


Neural Relational Inference for Interacting Systems

Learning the latent interaction graph of a dynamical system

T. Kipf*, E. Fetaya*, K. Wang, M. Welling, R. Zemel, Neural Relational Inference for Interacting Systems, (ICML 2018) [Link, PDF (arXiv), code], *equal contribution.


MolGAN: An implicit generative model for small molecular graphs

Learning to generate molecular graphs using a combined GAN/RL-based objective

N. De Cao, T. Kipf, MolGAN: An implicit generative model for small molecular graphs, ICML Deep Generative Models Workshop (2018) [Link, PDF (arXiv), code].


Hyperspherical Variational Auto-Encoders

Learning hyperspherical latent spaces

T. R. Davidson*, L. Falorsi*, N. De Cao*, T. Kipf, J. M. Tomczak, Hyperspherical Variational Auto-Encoders, (UAI 2018), Plenary Talk [Link, PDF (arXiv), code, blog], *equal contribution.


Extraction of Airways using Graph Neural Networks

Graph refinement with graph neural nets

R. Selvan, T. Kipf, M. Welling, J. H. Pedersen, J. Petersen, M. de Bruijne, Extraction of Airways using Graph Neural Networks, (MIDL 2018 - short paper) [Link, PDF (arXiv)].


Modeling Relational Data with Graph Convolutional Networks

Link prediction and entity classification on knowledge graphs

M. Schlichtkrull*, T. N. Kipf*, P. Bloem, R. vd Berg, I. Titov, M. Welling, Modeling Relational Data with Graph Convolutional Networks, (ESWC 2018), Best Student Research Paper [Link, PDF (arXiv), code], *equal contribution.


Graph Convolutional Matrix Completion

Collaborative filtering with side information in bipartite graphs

R. vd Berg, T. N. Kipf, M. Welling, Graph Convolutional Matrix Completion, KDD Deep Learning Day (2018), Spotlight Talk [Link, PDF (arXiv), code]


Semi-Supervised Classification with Graph Convolutional Networks

Neural networks for node classification on graphs

T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks (ICLR 2017) [Link, PDF (arXiv), code, blog]


Variational Graph Auto-Encoders

A latent variable model for graph-structured data

T. N. Kipf, M. Welling, Variational Graph Auto-Encoders, (NIPS Bayesian Deep Learning Workshop 2016) [Link, PDF (arXiv), code]


Recurrent Neural Networks for Graph-Based 3D Agglomeration

M.Sc. thesis (2016), Department of Connectomics, MPI for Brain Research

Thesis copy available here: [PDF (9 MB)]


For a full list, have a look at my Google Scholar page.

Curriculum vitae