Learning the Irreducible Representations of Commutative Lie Groups. - Taco Cohen; Max Welling (ICML, 2014)

Harmonic Exponential Families on Manifolds. - Taco Cohen; Max Welling (ICML, 2015)

Transformation Properties of Learned Visual Representations. - Taco S. Cohen; Max Welling (ICLR (Poster), 2015)

Group Equivariant Convolutional Networks. - Taco Cohen; Max Welling (ICML, 2016)

A New Method to Visualize Deep Neural Networks. - Luisa M. Zintgraf; Taco S. Cohen; Max Welling (CoRR, 2016)

Steerable CNNs. - Taco S. Cohen; Max Welling (ICLR (Poster), 2017)

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. - Luisa M. Zintgraf; Taco S. Cohen; Tameem Adel; Max Welling (ICLR (Poster), 2017)

Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. - A. Eck; Luisa M. Zintgraf; E. F. J. de Groot; Tim G. J. de Meij; Taco S. Cohen; P. H. M. Savelkoul; Max Welling; A. E. Budding (BMC Bioinform., 2017)

Convolutional Networks for Spherical Signals. - Taco Cohen; Mario Geiger; Jonas Köhler 001; Max Welling (CoRR, 2017)

Spherical CNNs. - Taco S. Cohen; Mario Geiger; Jonas Köhler 001; Max Welling (ICLR, 2018)

HexaConv. - Emiel Hoogeboom; Jorn W. T. Peters; Taco S. Cohen; Max Welling (ICLR (Poster), 2018)

Rotation Equivariant CNNs for Digital Pathology. - Bastiaan S. Veeling; Jasper Linmans; Jim Winkens; Taco Cohen; Max Welling (MICCAI (2), 2018)

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. - Maurice Weiler; Mario Geiger; Max Welling; Wouter Boomsma; Taco Cohen (NeurIPS, 2018)

Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). - Taco S. Cohen; Mario Geiger; Maurice Weiler (CoRR, 2018)

3D G-CNNs for Pulmonary Nodule Detection. - Marysia Winkels; Taco S. Cohen (CoRR, 2018)

Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks. - Jasper Linmans; Jim Winkens; Bastiaan S. Veeling; Taco S. Cohen; Max Welling (CoRR, 2018)

Explorations in Homeomorphic Variational Auto-Encoding. - Luca Falorsi; Pim de Haan; Tim R. Davidson; Nicola De Cao; Maurice Weiler; Patrick Forré; Taco S. Cohen (CoRR, 2018)

Video Compression With Rate-Distortion Autoencoders. - AmirHossein Habibian; Ties van Rozendaal; Jakub M. Tomczak; Taco Cohen (ICCV, 2019)

Gauge Equivariant Convolutional Networks and the Icosahedral CNN. - Taco Cohen; Maurice Weiler; Berkay Kicanaoglu; Max Welling (ICML, 2019)

A General Theory of Equivariant CNNs on Homogeneous Spaces. - Taco S. Cohen; Mario Geiger; Maurice Weiler (NeurIPS, 2019)

Covariance in Physics and Convolutional Neural Networks. - Miranda C. N. Cheng; Vassilis Anagiannis; Maurice Weiler; Pim de Haan; Taco S. Cohen; Max Welling (CoRR, 2019)

Pulmonary nodule detection in CT scans with equivariant CNNs. - Marysia Winkels; Taco S. Cohen (Medical Image Anal., 2019)

Feedback Recurrent Autoencoder for Video Compression. - Adam Golinski; Reza Pourreza 002; Yang Yang 010; Guillaume Sautière; Taco S. Cohen (ACCV (4), 2020)

Lossy Compression with Distortion Constrained Optimization. - Ties van Rozendaal; Guillaume Sautière; Taco S. Cohen (CVPR Workshops, 2020)

Adversarial Distortion for Learned Video Compression. - Vijay Veerabadran; Reza Pourreza 002; AmirHossein Habibian; Taco Cohen (CVPR Workshops, 2020)

Feedback Recurrent Autoencoder. - Yang Yang 010; Guillaume Sautière; J. Jon Ryu; Taco S. Cohen (ICASSP, 2020)

Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. - Adeel Pervez; Taco Cohen; Efstratios Gavves (ICML, 2020)

Parallelized Rate-Distortion Optimized Quantization Using Deep Learning. - Dana Kianfar; Auke J. Wiggers; Amir Said; Reza Pourreza 002; Taco Cohen (MMSP, 2020)

Natural Graph Networks. - Pim de Haan; Taco S. Cohen; Max Welling (NeurIPS, 2020)

Learning Discrete Distributions by Dequantization. - Emiel Hoogeboom; Taco S. Cohen; Jakub M. Tomczak (CoRR, 2020)

A Data and Compute Efficient Design for Limited-Resources Deep Learning. - Mirgahney Mohamed; Gabriele Cesa; Taco S. Cohen; Max Welling (CoRR, 2020)

Teacher-Student Curriculum Learning. - Tambet Matiisen; Avital Oliver; Taco Cohen; John Schulman (IEEE Trans. Neural Networks Learn. Syst., 2020)

Skip-Convolutions for Efficient Video Processing. - Amirhossein Habibian; Davide Abati; Taco S. Cohen; Babak Ehteshami Bejnordi (CVPR, 2021)

Extending Neural P-frame Codecs for B-frame Coding. - Reza Pourreza 002; Taco Cohen (ICCV, 2021)

Progressive Neural Image Compression With Nested Quantization And Latent Ordering. - Yadong Lu; Yinhao Zhu; Yang Yang 010; Amir Said; Taco S. Cohen (ICIP, 2021)

Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. - Pim de Haan; Maurice Weiler; Taco Cohen; Max Welling (ICLR, 2021)

Overfitting for Fun and Profit: Instance-Adaptive Data Compression. - Ties van Rozendaal; Iris A. M. Huijben; Taco Cohen (ICLR, 2021)

Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces. - Hilmi E. Egilmez; Ankitesh K. Singh; Muhammed Z. Coban; Marta Karczewicz; Yinhao Zhu; Yang Yang 010; Amir Said; Taco S. Cohen (CoRR, 2021)

A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space. - Ankitesh K. Singh; Hilmi E. Egilmez; Reza Pourreza 002; Muhammed Z. Coban; Marta Karczewicz; Taco S. Cohen (CoRR, 2021)

Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. - Michael M. Bronstein; Joan Bruna; Taco Cohen; Petar Velickovic (CoRR, 2021)

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. - Ties van Rozendaal; Johann Brehmer; Yunfan Zhang; Reza Pourreza 002; Taco S. Cohen (CoRR, 2021)

Implicit Neural Video Compression. - Yunfan Zhang; Ties van Rozendaal; Johann Brehmer; Markus Nagel; Taco Cohen (CoRR, 2021)

Region-of-Interest Based Neural Video Compression. - Yura Perugachi-Diaz; Guillaume Sautière; Davide Abati; Yang Yang 010; Amirhossein Habibian; Taco S. Cohen (BMVC, 2022)

Efficient Neural Causal Discovery without Acyclicity Constraints. - Phillip Lippe; Taco Cohen; Efstratios Gavves (ICLR, 2022)

Transformer-based Transform Coding. - Yinhao Zhu; Yang Yang 010; Taco Cohen (ICLR, 2022)

CITRIS: Causal Identifiability from Temporal Intervened Sequences. - Phillip Lippe; Sara Magliacane; Sindy Löwe; Yuki M. Asano; Taco Cohen; Stratis Gavves (ICML, 2022)

A PAC-Bayesian Generalization Bound for Equivariant Networks. - Arash Behboodi; Gabriele Cesa; Taco S. Cohen (NeurIPS, 2022)

Weakly supervised causal representation learning. - Johann Brehmer; Pim de Haan; Phillip Lippe; Taco S. Cohen (NeurIPS, 2022)

On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. - Gabriele Cesa; Arash Behboodi; Taco S. Cohen; Max Welling (NeurIPS, 2022)

CITRIS: Causal Identifiability from Temporal Intervened Sequences. - Phillip Lippe; Sara Magliacane; Sindy Löwe; Yuki M. Asano; Taco Cohen; Efstratios Gavves (CoRR, 2022)

iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects. - Phillip Lippe; Sara Magliacane; Sindy Löwe; Yuki M. Asano; Taco Cohen; Efstratios Gavves (CoRR, 2022)

Towards a Grounded Theory of Causation for Embodied AI. - Taco Cohen (CoRR, 2022)

Deconfounded Imitation Learning. - Risto Vuorio; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen; Pim de Haan (CoRR, 2022)

Guest Editorial: Non-Euclidean Machine Learning. - Stefanos Zafeiriou; Michael M. Bronstein; Taco Cohen; Oriol Vinyals; Le Song; Jure Leskovec; Pietro Liò; Joan Bruna; Marco Gori (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Equivariant Mesh Attention Networks. - Sourya Basu; Jose Gallego-Posada; Francesco Viganò; James Rowbottom; Taco Cohen (Trans. Mach. Learn. Res., 2022)

Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. - Phillip Lippe; Sara Magliacane; Sindy Löwe; Yuki M. Asano; Taco Cohen; Efstratios Gavves (ICLR, 2023)

On the Expressive Power of Geometric Graph Neural Networks. - Chaitanya K. Joshi; Cristian Bodnar; Simon V. Mathis; Taco Cohen; Pietro Lio (ICML, 2023)

EDGI: Equivariant Diffusion for Planning with Embodied Agents. - Johann Brehmer; Joey Bose; Pim de Haan; Taco S. Cohen (NeurIPS, 2023)

Geometric Algebra Transformer. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco S. Cohen (NeurIPS, 2023)

BISCUIT: Causal Representation Learning from Binary Interactions. - Phillip Lippe; Sara Magliacane; Sindy Löwe; Yuki M. Asano; Taco Cohen; Efstratios Gavves (UAI, 2023)

Geometric Algebra Transformers. - Johann Brehmer; Pim de Haan; Sönke Behrends; Taco Cohen (CoRR, 2023)

Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation. - Pietro Mazzaglia; Taco Cohen; Daniel Dijkman (CoRR, 2023)

FoMo Rewards: Can we cast foundation models as reward functions? - Ekdeep Singh Lubana; Johann Brehmer; Pim de Haan; Taco Cohen (CoRR, 2023)

A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. - Alexandre Duval; Simon V. Mathis; Chaitanya K. Joshi; Victor Schmidt; Santiago Miret; Fragkiskos D. Malliaros; Taco Cohen; Pietro Lio; Yoshua Bengio; Michael M. Bronstein (CoRR, 2023)

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. - Ties van Rozendaal; Johann Brehmer; Yunfan Zhang; Reza Pourreza 002; Auke J. Wiggers; Taco Cohen (Trans. Mach. Learn. Res., 2023)

Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. - Pim de Haan; Taco Cohen; Johann Brehmer (AISTATS, 2024)

CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay. - Natasha Butt; Blazej Manczak; Auke J. Wiggers; Corrado Rainone; David W. Zhang; Michaël Defferrard; Taco Cohen (ICML, 2024)

Information-driven Affordance Discovery for Efficient Robotic Manipulation. - Pietro Mazzaglia; Taco Cohen; Daniel Dijkman (ICRA, 2024)

RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning. - Jonas Gehring; Kunhao Zheng; Jade Copet; Vegard Mella; Taco Cohen; Gabriel Synnaeve (CoRR, 2024)

What Makes Large Language Models Reason in (Multi-Turn) Code Generation? - Kunhao Zheng; Juliette Decugis; Jonas Gehring; Taco Cohen; Benjamin Négrevergne; Gabriel Synnaeve (CoRR, 2024)

Deconfounding Imitation Learning with Variational Inference. - Risto Vuorio; Pim de Haan; Johann Brehmer; Hanno Ackermann; Daniel Dijkman; Taco Cohen (Trans. Mach. Learn. Res., 2024)

The KoLMogorov Test: Compression by Code Generation. - Ori Yoran; Kunhao Zheng; Fabian Gloeckle; Jonas Gehring; Gabriel Synnaeve; Taco Cohen (ICLR, 2025)

What Makes Large Language Models Reason in (Multi-Turn) Code Generation? - Kunhao Zheng; Juliette Decugis; Jonas Gehring; Taco Cohen; Benjamin NéXuanjinggrevergne; Gabriel Synnaeve (ICLR, 2025)

Does equivariance matter at scale? - Johann Brehmer; Sönke Behrends; Pim de Haan; Taco Cohen (Trans. Mach. Learn. Res., 2025)

Soft Policy Optimization: Online Off-Policy RL for Sequence Models. - Taco Cohen; David W. Zhang; Kunhao Zheng; Yunhao Tang; Rémi Munos; Gabriel Synnaeve (CoRR, 2025)

RL-finetuning LLMs from on- and off-policy data with a single algorithm. - Yunhao Tang; Taco Cohen; David W. Zhang; Michal Valko; Rémi Munos (CoRR, 2025)

UMA: A Family of Universal Models for Atoms. - Brandon M. Wood; Misko Dzamba; Xiang Fu 005; Meng Gao; Muhammed Shuaibi; Luis Barroso-Luque; Kareem Abdelmaqsoud; Vahe Gharakhanyan; John R. Kitchin; Daniel S. Levine 003; Kyle Michel; Anuroop Sriram; et al. (CoRR, 2025)