Taco Cohen

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 52

Top Topics

Causal Discovery

Weakly Supervised

Geometric Algebra

Video Compression

Top Venues

Publications

Pietro Mazzaglia, Taco Cohen, Daniel Dijkman
Information-driven Affordance Discovery for Efficient Robotic Manipulation. CoRR (2024)
Pim de Haan, Taco Cohen, Johann Brehmer
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. AISTATS (2024)
Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay. CoRR (2024)
Pietro Mazzaglia, Taco Cohen, Daniel Dijkman
Information-driven Affordance Discovery for Efficient Robotic Manipulation. ICRA (2024)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
BISCUIT: Causal Representation Learning from Binary Interactions. UAI (2023)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. ICLR (2023)
Pim de Haan, Taco Cohen, Johann Brehmer
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. CoRR (2023)
Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Auke J. Wiggers, Taco Cohen
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. Trans. Mach. Learn. Res. 2023 (2023)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
BISCUIT: Causal Representation Learning from Binary Interactions. CoRR (2023)
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Lio, Yoshua Bengio, Michael M. Bronstein
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems. CoRR (2023)
Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen
EDGI: Equivariant Diffusion for Planning with Embodied Agents. CoRR (2023)
Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan, Taco Cohen
FoMo Rewards: Can we cast foundation models as reward functions? CoRR (2023)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio
On the Expressive Power of Geometric Graph Neural Networks. ICML (2023)
Pietro Mazzaglia, Taco Cohen, Daniel Dijkman
Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation. CoRR (2023)
Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò
On the Expressive Power of Geometric Graph Neural Networks. CoRR (2023)
Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen
Geometric Algebra Transformers. CoRR (2023)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
CITRIS: Causal Identifiability from Temporal Intervened Sequences. CoRR (2022)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects. CoRR (2022)
Yinhao Zhu, Yang Yang, Taco Cohen
Transformer-based Transform Coding. ICLR (2022)
Arash Behboodi, Gabriele Cesa, Taco Cohen
A PAC-Bayesian Generalization Bound for Equivariant Networks. CoRR (2022)
Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen
Equivariant Mesh Attention Networks. CoRR (2022)
Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen
Weakly supervised causal representation learning. CoRR (2022)
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Stratis Gavves
CITRIS: Causal Identifiability from Temporal Intervened Sequences. ICML (2022)
Phillip Lippe, Taco Cohen, Efstratios Gavves
Efficient Neural Causal Discovery without Acyclicity Constraints. ICLR (2022)
Taco Cohen
Towards a Grounded Theory of Causation for Embodied AI. CoRR (2022)
Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan
Deconfounded Imitation Learning. CoRR (2022)
Stefanos Zafeiriou, Michael M. Bronstein, Taco Cohen, Oriol Vinyals, Le Song, Jure Leskovec, Pietro Liò, Joan Bruna, Marco Gori
Guest Editorial: Non-Euclidean Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44 (2) (2022)
Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen
Equivariant Mesh Attention Networks. Trans. Mach. Learn. Res. 2022 (2022)
Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR (2021)
Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen
Implicit Neural Video Compression. CoRR (2021)
Reza Pourreza, Taco Cohen
Extending Neural P-frame Codecs for B-frame Coding. ICCV (2021)
Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. ICLR (2021)
Ties van Rozendaal, Iris A. M. Huijben, Taco Cohen
Overfitting for Fun and Profit: Instance-Adaptive Data Compression. ICLR (2021)
Phillip Lippe, Taco Cohen, Efstratios Gavves
Efficient Neural Causal Discovery without Acyclicity Constraints. CoRR (2021)
Adeel Pervez, Taco Cohen, Efstratios Gavves
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. ICML (2020)
Pim de Haan, Taco Cohen, Max Welling
Natural Graph Networks. CoRR (2020)
Vijay Veerabadran, Reza Pourreza, AmirHossein Habibian, Taco Cohen
Adversarial Distortion for Learned Video Compression. CVPR Workshops (2020)
Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. CoRR (2020)
Vijay Veerabadran, Reza Pourreza, AmirHossein Habibian, Taco Cohen
Adversarial Distortion for Learned Video Compression. CoRR (2020)
Dana Kianfar, Auke Wiggers, Amir Said, Reza Pourreza, Taco Cohen
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning. MMSP (2020)
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
Teacher-Student Curriculum Learning. IEEE Trans. Neural Networks Learn. Syst. 31 (9) (2020)
Dana Kianfar, Auke Wiggers, Amir Said, Reza Pourreza, Taco Cohen
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning. CoRR (2020)
Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. ICML (2019)
AmirHossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco Cohen
Video Compression With Rate-Distortion Autoencoders. ICCV (2019)
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR (2018)
Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS (2018)
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling
Rotation Equivariant CNNs for Digital Pathology. CoRR (2018)
Taco Cohen, Mario Geiger, Maurice Weiler
A General Theory of Equivariant CNNs on Homogeneous Spaces. CoRR (2018)
Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling
Rotation Equivariant CNNs for Digital Pathology. MICCAI (2) (2018)
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
Teacher-Student Curriculum Learning. CoRR (2017)
Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling
Convolutional Networks for Spherical Signals. CoRR (2017)
Taco Cohen, Max Welling
Group Equivariant Convolutional Networks. ICML (2016)
Taco Cohen, Max Welling
Harmonic Exponential Families on Manifolds. ICML (2015)
Taco Cohen, Max Welling
Learning the Irreducible Representations of Commutative Lie Groups. CoRR (2014)
Taco Cohen, Max Welling
Learning the Irreducible Representations of Commutative Lie Groups. ICML (2014)