I’m a machine learning researcher at Qualcomm and finishing my PhD in Machine Learning at the University of Amsterdam where I work with Max Welling. I received a BSc in CS from Utrecht University and a MSc in AI from the University of Amsterdam (both cum laude). In 2013, I cofounded Scyfer, a company specialized in deep active learning (acquired by Qualcomm in 2017). During summer 2015 I worked on unsupervised learning of equivariant representations with Geoff Hinton at Google DeepMind. During fall 2016 / spring 2017 I spent some time at OpenAI. I received the 2017 Google PhD Fellowship. Here is my CV.

My research is focussed on learning of equivariant representations for data-efficient deep learning. Besides improving data-efficiency, “equivariance to symmetry transformations” provides one of the first rational design principles for deep neural networks, and allows them to be more easily interpreted in geometrical terms than ordinary black-box networks.

I’m very excited by the application of these methods to medical image analysis, where data-efficiency is critical. More broadly, I’m fascinated by all things related to human cognition and perception, pure mathematics, and theoretical physics.

twitter: @TacoCohen

## Publications

T.S. Cohen, M. Geiger, M. Weiler, *The Quite General Theory of Equivariant Convolutional Networks* (under review @ NIPS), 2018

L. Falorsi, P. de Haan, T. R. Davidson, N. De Cao, M. Weiler, P. Forré and T. S. Cohen, *Explorations in Homeomorphic Variational Auto-Encoding, *ICML Workshop on Theoretical Foundations and Applications of Generative Models, 2018

[ArXiv]

M. Weiler, W. Boomsma, M. Geiger, M. Welling, T.S. Cohen, *3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data* (under review @ NIPS), 2018

[ArXiv]

T.S. Cohen, M. Geiger, M. Weiler, * Intertwiners between Induced Representations (with applications to the theory of equivariant neural networks), *ArXiv preprint 1803.10743, 2018.

[ArXiv]

[ArXiv]

M. Winkels, T.S. Cohen, * 3D G-CNNs for Pulmonary Nodule Detection.* International Conference on Medical Imaging with Deep Learning (MIDL), 2018.

[ArXiv]

M. Winkels, T.S. Cohen, *3D Group-Equivariant Neural Networks for Octahedral and Square Prism Symmetry Groups*, FAIM/ICML Workshop on Towards learning with limited labels: Equivariance, Invariance, and Beyond, 2018.

B.S. Veeling, J. Linmans, J. Winkens, T.S. Cohen, M. Welling, *Rotation Equivariant CNNs for Digital Pathology*. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018.

[ArXiv] [PCam repo]

J. Winkens, J. Linmans, B.S. Veeling, T.S. Cohen, M. Welling, *Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks. *International Conference on Medical Imaging with Deep Learning (MIDL workshop), 2018.

[pdf]

J. Linmans, J. Winkens, B.S. Veeling, T.S. Cohen, M. Welling, *Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks*, FAIM/ICML Workshop on Towards learning with limited labels: Equivariance, Invariance, and Beyond, 2018.

T.S. Cohen, M. Geiger, J. Koehler, M. Welling, * Spherical CNNs. *ICLR 2018 (

**Best paper award**).

[pdf] [code]

E. Hoogeboom, J.W.T. Peters, T.S. Cohen, M. Welling, *HexaConv. *ICLR 2018.

[pdf]

T.S. Cohen, M. Geiger, J. Koehler, M. Welling, *Convolutional Networks for Spherical Signals.* In Principled Approaches to Deep Learning Workshop ICML 2017.

[ArXiv]

A. Eck, L.M. Zintgraf, E.F.J. de Groot, T.G.J. de Meij, T.S. Cohen, P.H.M. Savelkoul, M. Welling, A.E. Budding, *Interpretation of microbiota-based diagnostics by explaining individual classifier decisions,* BMC Bioinformatics, 2017.

[pubmed]

T. Matiisen, A. Oliver, T.S. Cohen, J. Schulman, *Teacher-Student Curriculum Learning*. Deep Reinforcement Learning Symposium, NIPS 2017

[ArXiv]

T.S. Cohen, M. Welling, ** Steerable CNNs**. International Conference on Learning Representations (ICLR), 2017

[pdf]

L.M. Zintgraf, T.S. Cohen, T. Adel, M. Welling, *Visualizing Deep Neural Network Decisions: Prediction Difference Analysis.* International Conference on Learning Representations (ICLR), 2017

[pdf]

T. Adel, T.S. Cohen, M. Caan, M. Welling, *3D Scattering Transforms for Disease Classification in Neuroimaging*. Neuroimage: clinical, 2017.

[pubmed]

T.S. Cohen, M. Welling, * Group Equivariant Convolutional Networks.* Proceedings of the International Conference on Machine Learning (ICML), 2016

[pdf] [supp. mat.] [code for experiments] [G-Conv code]

L.M. Zintgraf, T.S. Cohen, M. Welling, *A New Method to Visualize Deep Neural Networks.* ArXiv preprint 1603.02518, 2016

[ArXiv]

T.S. Cohen, M. Welling, *Harmonic Exponential Families on Manifolds.* Proceedings of the International Conference on Machine Learning (ICML), 2015

[pdf] [supp. mat.]

T.S. Cohen, M. Welling, *Transformation Properties of Learned Visual Representations. *International Conference on Learning Representations (ICLR), 2015.

[ArXiv]

T.S. Cohen, M. Welling, *Learning the Irreducible Representations of Commutative Lie Groups*. Proceedings of the International Conference on Machine Learning (ICML), 2014.

[pdf] [supp. mat.]

T.S. Cohen, *Learning Transformation Groups and their Invariants*. Master’s thesis, University of Amsterdam, 2013. (**1st place University of Amsterdam thesis prize 2014)**

[pdf]