doi.bio/fabian_b_fuchs

Fabian B. Fuchs

Fabian B. Fuchs is a researcher and PhD student in the Department of Engineering Science at the University of Oxford. His research interests include neural networks, artificial intelligence, unsupervised learning, and 3D data manipulation.

Biography

Fabian B. Fuchs is a PhD student in the Department of Engineering Science at the University of Oxford. He is also a member of the ResearchGate scientific community. His research focuses on leveraging symmetries and geometric inductive bias by building equivariant and invariant neural networks.

Research and Publications

Fuchs has published several papers in the field of machine learning and artificial intelligence. Some of his notable works include:

Equilibrium Aggregation (2022): Co-authored with Servey Bartunov and Timothy P. Lillicrap, this paper was presented at the Conference on Uncertainty in Artificial Intelligence (UAI).
Universal Approximation of Functions on Sets (2022): Co-authored with Edward Wagstaff, Martin Engelcke, Michael Osborne, and Ingmar Posner, this paper was published in the Journal for Machine Learning Research (JMLR).
E(n) Equivariant Normalizing Flows for Molecule Generation in 3D (2021): Co-authored with Victor Garcia Satorras, Emiel Hoogeboom, Ingmar Posner, and Max Welling, this work was presented at the Conference on Neural Information Processing Systems (NeurIPS).
Iterative SE(3)-Transformers (2021): Co-authored with Edward Wagstaff, Justas Dauparas, and Ingmar Posner, this oral presentation was given at the International Conference on Geometric Science of Information (GSI) in Paris.
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020): Co-authored with Daniel E. Worrall, Volker Fischer, and Max Welling, this paper was presented at the Conference on Neural Information Processing Systems (NeurIPS).
On the Limitations of Representing Functions on Sets (2019): Co-authored with Edward Wagstaff, Martin Engelcke, Ingmar Posner, and Michael Osborne, this paper was presented at the International Conference on Machine Learning (ICML).
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019): Co-authored with Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, and Ingmar Posner, this work was presented at the Sets and Partitions Workshop @ NeurIPS.
Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing (2019): Co-authored with Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, and Ingmar Posner, this paper was presented at the British Machine Vision Conference (BMVC).
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking (2018): Co-authored with Oliver Groth, Ingmar Posner, and Andrea Vedaldi, this paper was presented at the European Conference on Computer Vision (ECCV).

References

Fabian B Fuchs - ResearchGate Profile. (n.d.). Retrieved June 4, 2024, from https://www.researchgate.net/profile/Fabian-Fuchs
Fabian B Fuchs - GitHub Profile. (n.d.). Retrieved June 4, 2024, from https://fabianfuchsml.github.io/CVFabianFuchs.pdf

- Fabian B Fuchs - LinkedIn Profile. (n.d.). Retrieved March 24, 2024, from https://uk.linkedin.com/in/fabian-fuchs-781426137

Fabian B. Fuchs

Fabian B. Fuchs is a PhD student and Master of Science at the University of Oxford's Department of Engineering Science. His research focuses on leveraging symmetries and geometric inductive bias by building equivariant and invariant neural networks.

Biography

Fabian B. Fuchs is a researcher in the field of machine learning and artificial intelligence. He is currently pursuing his PhD and Master of Science at the University of Oxford, in the Department of Engineering Science.

Publications

Fuchs has authored or co-authored several papers in the field of machine learning, including:

Equilibrium Aggregation: Encoding Sets via Optimization (2022). Co-authored with Sergey Bartunov and Timothy Lillicrap. Presented at the Conference on Uncertainty in Artificial Intelligence (UAI).
Universal Approximation of Functions on Sets (2022). Co-authored with Edward Wagstaff, Martin Engelcke, Michael A. Osborne, and Ingmar Posner. Published in the Journal for Machine Learning Research (JMLR).
E(n) Equivariant Normalizing Flows (2021). Co-authored with Victor Garcia Satorras, Emiel Hoogeboom, Ingmar Posner, and Max Welling. Presented at the Conference on Neural Information Processing Systems (NeurIPS).
Iterative SE(3)-Transformers (2021). Co-authored with Edward Wagstaff, Justas Dauparas, and Ingmar Posner. Presented at the International Conference on Geometric Science of Information (GSI) in Paris.
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020). Co-authored with Daniel E. Worrall, Volker Fischer, and Max Welling. Presented at the Conference on Neural Information Processing Systems (NeurIPS).
On the Limitations of Representing Functions on Sets (2019). Co-authored with Edward Wagstaff, Martin Engelcke, Ingmar Posner, and Michael Osborne. Presented at the International Conference on Machine Learning (ICML).
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019). Co-authored with Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, and Ingmar Posner. Presented at the Sets and Partitions Workshop @ NeurIPS.
Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing (2019). Co-authored with Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, and Ingmar Posner. Presented at the British Machine Vision Conference (BMVC).

- ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking (2018). Co-authored with Oliver Groth, Ingmar Posner, and Andrea Vedaldi. Presented at the European Conference on Computer Vision (ECCV).

Fabian B. Fuchs

Biography

Publications

Fuchs has authored or co-authored several papers in the field of machine learning, including:

Equilibrium Aggregation: Encoding Sets via Optimization (2022). Co-authored with Sergey Bartunov and Timothy Lillicrap. Presented at the Conference on Uncertainty in Artificial Intelligence (UAI).
Universal Approximation of Functions on Sets (2022). Co-authored with Edward Wagstaff, Martin Engelcke, Michael A. Osborne, and Ingmar Posner. Published in the Journal for Machine Learning Research (JMLR).
E(n) Equivariant Normalizing Flows (2021). Co-authored with Victor Garcia Satorras, Emiel Hoogeboom, Ingmar Posner, and Max Welling. Presented at the Conference on Neural Information Processing Systems (NeurIPS).
Iterative SE(3)-Transformers (2021). Co-authored with Edward Wagstaff, Justas Dauparas, and Ingmar Posner. Presented at the International Conference on Geometric Science of Information (GSI) in Paris.
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks (2020). Co-authored with Daniel E. Worrall, Volker Fischer, and Max Welling. Presented at the Conference on Neural Information Processing Systems (NeurIPS).
On the Limitations of Representing Functions on Sets (2019). Co-authored with Edward Wagstaff, Martin Engelcke, Ingmar Posner, and Michael Osborne. Presented at the International Conference on Machine Learning (ICML).
End-to-end Recurrent Multi-Object Tracking and Trajectory Prediction with Relational Reasoning (2019). Co-authored with Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, and Ingmar Posner. Presented at the Sets and Partitions Workshop @ NeurIPS.
Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing (2019). Co-authored with Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, and Ingmar Posner. Presented at the British Machine Vision Conference (BMVC).
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking (2018). Co-authored with Oliver Groth, Ingmar Posner, and Andrea Vedaldi. Presented at the European Conference on Computer Vision (ECCV).

Co-Authors

Fuchs has collaborated with several notable researchers in the field, including Ingmar Posner, Max Welling, Edward Wagstaff, Martin Engelcke, and Michael A. Osborne, among others.