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ORCIDTaco Cohen
Taco S. Cohen
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- affiliation: University of Amsterdam, The Netherlands
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2020 – today
- 2024
[c40]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. AISTATS 2024: 3088-3096- [c39]Natasha Butt, Blazej Manczak, Auke J. Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen:
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay. ICML 2024 - [c38]Pietro Mazzaglia, Taco Cohen, Daniel Dijkman:
Information-driven Affordance Discovery for Efficient Robotic Manipulation. ICRA 2024: 7780-7787 - [i57]Natasha Butt, Blazej Manczak, Auke J. Wiggers, Corrado Rainone, David W. Zhang, Michaël Defferrard, Taco Cohen:
CodeIt: Self-Improving Language Models with Prioritized Hindsight Replay. CoRR abs/2402.04858 (2024) - [i56]Pietro Mazzaglia, Taco Cohen, Daniel Dijkman:
Information-driven Affordance Discovery for Efficient Robotic Manipulation. CoRR abs/2405.03865 (2024) - [i55]Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Taco Cohen, Gabriel Synnaeve:
RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning. CoRR abs/2410.02089 (2024) - 2023
- [j6]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) - [c37]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 - [c36]Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Lio:
On the Expressive Power of Geometric Graph Neural Networks. ICML 2023: 15330-15355 - [c35]Johann Brehmer, Joey Bose, Pim de Haan, Taco S. Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. NeurIPS 2023 - [c34]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco S. Cohen:
Geometric Algebra Transformer. NeurIPS 2023 - [c33]Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves:
BISCUIT: Causal Representation Learning from Binary Interactions. UAI 2023: 1263-1273 - [i54]Chaitanya K. Joshi, Cristian Bodnar, Simon V. Mathis, Taco Cohen, Pietro Liò:
On the Expressive Power of Geometric Graph Neural Networks. CoRR abs/2301.09308 (2023) - [i53]Johann Brehmer, Joey Bose, Pim de Haan, Taco Cohen:
EDGI: Equivariant Diffusion for Planning with Embodied Agents. CoRR abs/2303.12410 (2023) - [i52]Johann Brehmer, Pim de Haan, Sönke Behrends, Taco Cohen:
Geometric Algebra Transformers. CoRR abs/2305.18415 (2023) - [i51]Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves:
BISCUIT: Causal Representation Learning from Binary Interactions. CoRR abs/2306.09643 (2023) - [i50]Pietro Mazzaglia, Taco Cohen, Daniel Dijkman:
Uncertainty-driven Affordance Discovery for Efficient Robotics Manipulation. CoRR abs/2308.14915 (2023) - [i49]Pim de Haan, Taco Cohen, Johann Brehmer:
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers. CoRR abs/2311.04744 (2023) - [i48]Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan, Taco Cohen:
FoMo Rewards: Can we cast foundation models as reward functions? CoRR abs/2312.03881 (2023) - [i47]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 abs/2312.07511 (2023) - 2022
- [j5]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): 723-726 (2022) - [j4]Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen:
Equivariant Mesh Attention Networks. Trans. Mach. Learn. Res. 2022 (2022) - [c32]Yura Perugachi-Diaz, Guillaume Sautière, Davide Abati, Yang Yang, Amirhossein Habibian, Taco S. Cohen:
Region-of-Interest Based Neural Video Compression. BMVC 2022: 288 - [c31]Phillip Lippe, Taco Cohen, Efstratios Gavves:
Efficient Neural Causal Discovery without Acyclicity Constraints. ICLR 2022 - [c30]Yinhao Zhu, Yang Yang, Taco Cohen:
Transformer-based Transform Coding. ICLR 2022 - [c29]Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Stratis Gavves:
CITRIS: Causal Identifiability from Temporal Intervened Sequences. ICML 2022: 13557-13603 - [c28]Arash Behboodi, Gabriele Cesa, Taco S. Cohen:
A PAC-Bayesian Generalization Bound for Equivariant Networks. NeurIPS 2022 - [c27]Johann Brehmer, Pim de Haan, Phillip Lippe, Taco S. Cohen:
Weakly supervised causal representation learning. NeurIPS 2022 - [c26]Gabriele Cesa, Arash Behboodi, Taco S. Cohen, Max Welling:
On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane. NeurIPS 2022 - [i46]Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves:
CITRIS: Causal Identifiability from Temporal Intervened Sequences. CoRR abs/2202.03169 (2022) - [i45]Yura Perugachi-Diaz, Guillaume Sautière, Davide Abati, Yang Yang, Amirhossein Habibian, Taco S. Cohen:
Region-of-Interest Based Neural Video Compression. CoRR abs/2203.01978 (2022) - [i44]Johann Brehmer, Pim de Haan, Phillip Lippe, Taco Cohen:
Weakly supervised causal representation learning. CoRR abs/2203.16437 (2022) - [i43]Sourya Basu, Jose Gallego-Posada, Francesco Viganò, James Rowbottom, Taco Cohen:
Equivariant Mesh Attention Networks. CoRR abs/2205.10662 (2022) - [i42]Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves:
iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects. CoRR abs/2206.06169 (2022) - [i41]Taco Cohen:
Towards a Grounded Theory of Causation for Embodied AI. CoRR abs/2206.13973 (2022) - [i40]Arash Behboodi, Gabriele Cesa, Taco Cohen:
A PAC-Bayesian Generalization Bound for Equivariant Networks. CoRR abs/2210.13150 (2022) - [i39]Risto Vuorio, Johann Brehmer, Hanno Ackermann, Daniel Dijkman, Taco Cohen, Pim de Haan:
Deconfounded Imitation Learning. CoRR abs/2211.02667 (2022) - 2021
- [c25]Amirhossein Habibian, Davide Abati, Taco S. Cohen, Babak Ehteshami Bejnordi:
Skip-Convolutions for Efficient Video Processing. CVPR 2021: 2695-2704 - [c24]Reza Pourreza, Taco Cohen:
Extending Neural P-frame Codecs for B-frame Coding. ICCV 2021: 6660-6669 - [c23]Yadong Lu, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen:
Progressive Neural Image Compression With Nested Quantization And Latent Ordering. ICIP 2021: 539-543 - [c22]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. ICLR 2021 - [c21]Ties van Rozendaal, Iris A. M. Huijben, Taco Cohen:
Overfitting for Fun and Profit: Instance-Adaptive Data Compression. ICLR 2021 - [i38]Ties van Rozendaal, Iris A. M. Huijben, Taco S. Cohen:
Overfitting for Fun and Profit: Instance-Adaptive Data Compression. CoRR abs/2101.08687 (2021) - [i37]Yadong Lu, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen:
Progressive Neural Image Compression with Nested Quantization and Latent Ordering. CoRR abs/2102.02913 (2021) - [i36]Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Z. Coban, Marta Karczewicz, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen:
Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces. CoRR abs/2103.01760 (2021) - [i35]Reza Pourreza, Taco S. Cohen:
Extending Neural P-frame Codecs for B-frame Coding. CoRR abs/2104.00531 (2021) - [i34]Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza, Muhammed Z. Coban, Marta Karczewicz, Taco S. Cohen:
A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space. CoRR abs/2104.00807 (2021) - [i33]Amirhossein Habibian, Davide Abati, Taco S. Cohen, Babak Ehteshami Bejnordi:
Skip-Convolutions for Efficient Video Processing. CoRR abs/2104.11487 (2021) - [i32]Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Velickovic:
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. CoRR abs/2104.13478 (2021) - [i31]Phillip Lippe, Taco Cohen, Efstratios Gavves:
Efficient Neural Causal Discovery without Acyclicity Constraints. CoRR abs/2107.10483 (2021) - [i30]Ties van Rozendaal, Johann Brehmer, Yunfan Zhang, Reza Pourreza, Taco S. Cohen:
Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set. CoRR abs/2111.10302 (2021) - [i29]Yunfan Zhang, Ties van Rozendaal, Johann Brehmer, Markus Nagel, Taco Cohen:
Implicit Neural Video Compression. CoRR abs/2112.11312 (2021) - 2020
- [j3]Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman:
Teacher-Student Curriculum Learning. IEEE Trans. Neural Networks Learn. Syst. 31(9): 3732-3740 (2020) - [c20]Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautière, Taco S. Cohen:
Feedback Recurrent Autoencoder for Video Compression. ACCV (4) 2020: 591-607 - [c19]Ties van Rozendaal, Guillaume Sautière, Taco S. Cohen:
Lossy Compression with Distortion Constrained Optimization. CVPR Workshops 2020: 634-639 - [c18]Vijay Veerabadran, Reza Pourreza, AmirHossein Habibian, Taco Cohen:
Adversarial Distortion for Learned Video Compression. CVPR Workshops 2020: 640-644 - [c17]Yang Yang, Guillaume Sautière, J. Jon Ryu, Taco S. Cohen:
Feedback Recurrent Autoencoder. ICASSP 2020: 3347-3351 - [c16]Adeel Pervez, Taco Cohen, Efstratios Gavves:
Low Bias Low Variance Gradient Estimates for Boolean Stochastic Networks. ICML 2020: 7632-7640 - [c15]Dana Kianfar, Auke J. Wiggers, Amir Said, Reza Pourreza, Taco Cohen:
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning. MMSP 2020: 1-6 - [c14]Pim de Haan, Taco S. Cohen, Max Welling:
Natural Graph Networks. NeurIPS 2020 - [i28]Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak:
Learning Discrete Distributions by Dequantization. CoRR abs/2001.11235 (2020) - [i27]Pim de Haan, Maurice Weiler, Taco Cohen, Max Welling:
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs. CoRR abs/2003.05425 (2020) - [i26]Adam Golinski, Reza Pourreza, Yang Yang, Guillaume Sautière, Taco S. Cohen:
Feedback Recurrent Autoencoder for Video Compression. CoRR abs/2004.04342 (2020) - [i25]Vijay Veerabadran, Reza Pourreza, AmirHossein Habibian, Taco Cohen:
Adversarial Distortion for Learned Video Compression. CoRR abs/2004.09508 (2020) - [i24]Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen, Max Welling:
A Data and Compute Efficient Design for Limited-Resources Deep Learning. CoRR abs/2004.09691 (2020) - [i23]Ties van Rozendaal, Guillaume Sautière, Taco S. Cohen:
Lossy Compression with Distortion Constrained Optimization. CoRR abs/2005.04064 (2020) - [i22]Pim de Haan, Taco Cohen, Max Welling:
Natural Graph Networks. CoRR abs/2007.08349 (2020) - [i21]Dana Kianfar, Auke J. Wiggers, Amir Said, Reza Pourreza, Taco Cohen:
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning. CoRR abs/2012.06380 (2020)
2010 – 2019
- 2019
- [j2]Marysia Winkels, Taco S. Cohen:
Pulmonary nodule detection in CT scans with equivariant CNNs. Medical Image Anal. 55: 15-26 (2019) - [c13]AmirHossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco Cohen:
Video Compression With Rate-Distortion Autoencoders. ICCV 2019: 7032-7041 - [c12]Taco Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. ICML 2019: 1321-1330 - [c11]Taco S. Cohen, Mario Geiger, Maurice Weiler:
A General Theory of Equivariant CNNs on Homogeneous Spaces. NeurIPS 2019: 9142-9153 - [i20]Taco S. Cohen, Maurice Weiler, Berkay Kicanaoglu, Max Welling:
Gauge Equivariant Convolutional Networks and the Icosahedral CNN. CoRR abs/1902.04615 (2019) - [i19]Miranda C. N. Cheng, Vassilis Anagiannis, Maurice Weiler, Pim de Haan, Taco S. Cohen, Max Welling:
Covariance in Physics and Convolutional Neural Networks. CoRR abs/1906.02481 (2019) - [i18]AmirHossein Habibian, Ties van Rozendaal, Jakub M. Tomczak, Taco S. Cohen:
Video Compression With Rate-Distortion Autoencoders. CoRR abs/1908.05717 (2019) - [i17]Yang Yang, Guillaume Sautière, J. Jon Ryu, Taco S. Cohen:
Feedback Recurrent AutoEncoder. CoRR abs/1911.04018 (2019) - 2018
- [c10]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. ICLR 2018 - [c9]Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling:
HexaConv. ICLR (Poster) 2018 - [c8]Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling:
Rotation Equivariant CNNs for Digital Pathology. MICCAI (2) 2018: 210-218 - [c7]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. NeurIPS 2018: 10402-10413 - [i16]Taco S. Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Spherical CNNs. CoRR abs/1801.10130 (2018) - [i15]Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling:
HexaConv. CoRR abs/1803.02108 (2018) - [i14]Taco S. Cohen, Mario Geiger, Maurice Weiler:
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks). CoRR abs/1803.10743 (2018) - [i13]Marysia Winkels, Taco S. Cohen:
3D G-CNNs for Pulmonary Nodule Detection. CoRR abs/1804.04656 (2018) - [i12]Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, Max Welling:
Rotation Equivariant CNNs for Digital Pathology. CoRR abs/1806.03962 (2018) - [i11]Jasper Linmans, Jim Winkens, Bastiaan S. Veeling, Taco S. Cohen, Max Welling:
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks. CoRR abs/1807.00583 (2018) - [i10]Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco Cohen:
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. CoRR abs/1807.02547 (2018) - [i9]Luca Falorsi, Pim de Haan, Tim R. Davidson, Nicola De Cao, Maurice Weiler, Patrick Forré, Taco S. Cohen:
Explorations in Homeomorphic Variational Auto-Encoding. CoRR abs/1807.04689 (2018) - [i8]Taco Cohen, Mario Geiger, Maurice Weiler:
A General Theory of Equivariant CNNs on Homogeneous Spaces. CoRR abs/1811.02017 (2018) - 2017
- [j1]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:
Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinform. 18(1): 441:1-441:13 (2017) - [c6]Taco S. Cohen, Max Welling:
Steerable CNNs. ICLR (Poster) 2017 - [c5]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ICLR (Poster) 2017 - [i7]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. CoRR abs/1702.04595 (2017) - [i6]Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman:
Teacher-Student Curriculum Learning. CoRR abs/1707.00183 (2017) - [i5]Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling:
Convolutional Networks for Spherical Signals. CoRR abs/1709.04893 (2017) - 2016
- [c4]Taco Cohen, Max Welling:
Group Equivariant Convolutional Networks. ICML 2016: 2990-2999 - [i4]Taco S. Cohen, Max Welling:
Group Equivariant Convolutional Networks. CoRR abs/1602.07576 (2016) - [i3]Luisa M. Zintgraf, Taco S. Cohen, Max Welling:
A New Method to Visualize Deep Neural Networks. CoRR abs/1603.02518 (2016) - [i2]Taco S. Cohen, Max Welling:
Steerable CNNs. CoRR abs/1612.08498 (2016) - 2015
- [c3]Taco Cohen, Max Welling:
Harmonic Exponential Families on Manifolds. ICML 2015: 1757-1765 - [c2]Taco S. Cohen, Max Welling:
Transformation Properties of Learned Visual Representations. ICLR (Poster) 2015 - 2014
- [c1]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. ICML 2014: 1755-1763 - [i1]Taco Cohen, Max Welling:
Learning the Irreducible Representations of Commutative Lie Groups. CoRR abs/1402.4437 (2014)
Coauthor Index
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last updated on 2024-11-11 21:31 CET by the dblp team
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