I’m a machine learning researcher at Qualcomm. I received a BSc in CS from Utrecht University and a MSc in AI and PhD in Machine Learning (with Max Welling) from the University of Amsterdam (all three 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 a Google PhD Fellowship and was named one of 35 innovators under 35 by MIT Techreview in 2018. 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, drug discovery and various scientific applications, where data-efficiency is critical. More broadly, I’m fascinated by all things related to human cognition and perception, pure mathematics, and theoretical physics.

Recently I have started to explore causality and interactive learning.

#### Links

Twitter: @TacoCohen

Google Scholar: link

Geometric Deep Learning Book & Video Lectures: link

NeurIPS Tutorial on Equivariant Networks with Risi Kondor: link

awesome-equivariant-network: overview of papers & videos on equivariant networks

Article in Quanta / Wired (2020)

TWIML Podcast on Natural Graph Networks (2020)

## Selected Publications

T.S. Cohen, * Equivariant Convolutional Networks*, PhD Thesis, University of Amsterdam, 2021

[pdf] (

**Note: Part II contains a lot of new material, not published before**)

Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković, ** Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, **2021

[ArXiv, website, youtube lectures]

P. de Haan, T. Cohen, M. Welling, * Natural Graph Networks,* NeurIPS 2020

[ArXiv]

T.S. Cohen, M. Weiler, B. Kicanaoglu, M. Welling, * Gauge Equivariant Convolutional Networks and the Icosahedral CNN, *Proceedings of the International Conference on Machine Learning (ICML), 2019

*[ArXiv]*

T.S. Cohen, M. Geiger, M. Weiler, ** A General Theory of Equivariant CNNs on Homogeneous Spaces**, NeurIPS 2019

[ArXiv]

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

[ArXiv]

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

**Best paper award**).

[pdf] [code]

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

[pdf]

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]

## All Publications

S. Basu, J. Gallego-Posada, F. Viganò, J. Rowbottom, T. Cohen, *Equivariant Mesh Attention Networks*, 2022

[ArXiv]

J. Brehmer, P. De Haan, P. Lippe, T. Cohen, *Weakly supervised causal representation learning*, OSC Workshop @ ICLR, 2022

[ArXiv]

P. Lippe, S. Magliacane, S. Löwe, Y. M Asano, T. Cohen, Efstratios Gavves, *CITRIS: Causal Identifiability from Temporal Intervened Sequences, *OSC Workshop @ ICLR, 2022

[ArXiv]

Y. Zhang, T. van Rozendaal, J. Brehmer, M. Nagel, T. Cohen, *Implicit Neural Video Compression*, DGM4HSD Workshop @ ICLR, 2022

[ArXiv]

P. Lippe, T. Cohen, E. Gavves, *Efficient Neural Causal Discovery without Acyclicity Constraints,* ICLR 2022

[ArXiv]

Y. Zhu, Y. Yang, T. Cohen, *Transformer-based transform coding*, ICLR 2022

[pdf]

T.S. Cohen, *Equivariant Convolutional Networks*, PhD Thesis, University of Amsterdam, 2021

[pdf] (**Note: Part II contains a lot of new material, not published before**)

Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković, *Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, *2021

[ArXiv, website, youtube lectures]

Y. Zhang, T. van Rozendaal, J. Brehmer, M. Nagel, T. Cohen, *Implicit Neural Video Compression*, ArXiv 2021

[ArXiv]

T. van Rozendaal, J. Brehmer, Y. Zhang, R. Pourreza, T.S. Cohen, *Instance-Adaptive Video Compression: Improving Neural Codecs by Training **on the Test Set*, ArXiv 2021

[ArXiv]

A. K. Singh, H. E. Egilmez, R. Pourreza, M. Coban, M. Karczewicz, T. S. Cohen, *A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space*

[ArXiv]

H. E. Egilmez, A. K. Singh, M. Coban, M. Karczewicz, Y. Zhu, Y. Yang, A. Said, T. S. Cohen, *Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces, *IEEE Open Journal of Signal Processing, 2021.

[ArXiv]

R. Pourreza, T. Cohen, *Extending Neural P-frame Codecs for B-frame Coding*, ICCV 2021.

[ArXiv]

A. Habibian, D. Abati, T. Cohen, B. Ehteshami Bejnordi, *Skip-Convolutions for Efficient Video Processing, *CVPR 2021.

[ArXiv]

Y. Lu, Y. Zhu, Y. Yang, A. Said, T. Cohen, *Progressive Neural Image Compression with Nested Quantization and Latent Ordering, *ICIP 2021

[ArXiv]

T. van Rozendaal*, I.A.M. Huijben*, T. Cohen, *Overfitting for Fun and Profit: Instance-Adaptive Data Compression,* ICLR 2021

(*equal contribution)

[ArXiv]

P. de Haan, M. Weiler, T. Cohen, M. Welling, *Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs, *ICLR 2021 (spotlight)*
*[ArXiv]

E. Hoogeboom, T.S. Cohen, J.M. Tomczak, *Learning Discrete Distributions by Dequantization,* 4th AABI Symposium 2021

[ArXiv]

P. de Haan, T. Cohen, M. Welling, *Natural Graph Networks,* NeurIPS 2020

[ArXiv]

D. Kianfar, A. Wiggers, A. Said, R. Pourreza, T. Cohen, *Parallelized Rate-Distortion Optimized Quantization using Deep Learning,* IEEE MMSP 2020

[ArXiv]

A. Pervez, T. Cohen, E. Gavves, *Low Bias Low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks,* ICML 2020

[pdf]

A. Golinski*, R. Pourreza*, Y. Yang*, G. Sautiere, T. Cohen, *Feedback Recurrent Autoencoder for Video Compression*, ACCV 2020

(*equal contribution)

[ArXiv]

T. van Rozendaal, G Sautiere, T.S. Cohen, *Lossy Compression with Distortion Constrained Optimization,* Workshop and Challenge on Learned Image Compression (CLIC) at CVPR 2020.

[ArXiv]

V. Veerabadran, R. Pourreza, A. Habibian, T. Cohen, *Adversarial Distortion for Learned Video Compression*, Workshop and Challenge on Learned Image Compression (CLIC) at CVPR 2020.

[ArXiv]

M. Mohamed, G. Cesa, T.S. Cohen, M. Welling, *A Data and Compute Efficient Design for Limited-Resources Deep Learning*, Practical Machine Learning for Developing Countries Workshop (ICLR), 2020

[ArXiv]

Y. Yang, G. Sautière, J. Jon Ryu, T.S. Cohen,* Feedback Recurrent AutoEncoder*, International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020

[ArXiv]

A. Habibian, T. van Rozendaal, J. Tomczak, T.S. Cohen, *Video Compression with Rate-Distortion Autoencoders*, International Conference on Computer Vision (ICCV), 2019

[ArXiv]

Miranda C.N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling, *Covariance in Physics and Convolutional Networks,* Theoretical Physics for Deep Learning Workshop @ ICML, 2019

[ArXiv]

T.S. Cohen, M. Weiler, B. Kicanaoglu, M. Welling, *Gauge Equivariant Convolutional Networks and the Icosahedral CNN, *Proceedings of the International Conference on Machine Learning (ICML), 2019*
*[ArXiv]

T.S. Cohen, M. Geiger, M. Weiler, *A General Theory of Equivariant CNNs on Homogeneous Spaces*, NeurIPS 2019

[ArXiv]

M. Weiler, W. Boomsma, M. Geiger, M. Welling, T.S. Cohen, *3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data*, Advances in Neural Information Processing Systems (NeurIPS), 2018

[ArXiv] [code] [video]

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] [code]

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]

M. Winkels, T.S. Cohen, *Pulmonary Nodule Detection in CT Scans with Equivariant CNNs, *Medical Image Analysis, 2018.

[Link]

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] [Code]

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*. IEEE Transactions on Neural Networks and Learning Systems, 2019. (An earlier version was presented at the 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]