Thomas Kipf

PhD candidate

University of Amsterdam

I am a third-year PhD student in Deep Learning for Network Analysis at the University of Amsterdam, supervised by Prof. Max Welling. My main area of interest is large-scale inference for structured data and semi-supervised learning. I am also interested in reasoning and multi-agent reinforcement learning.

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 developed a deep interest in 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, arXiv:1802.04687 (2018) [Link, PDF (arXiv), code], *equal contribution.


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, arXiv:1804.00891 (2018) [Link, PDF (arXiv), code, blog], *equal contribution.


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) [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, arXiv:1706.02263 (2017) [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