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ORCIDFelix Dangel
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2020 – today
- 2024
[c7]Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood:
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. ICML 2024- [c6]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. ICML 2024 - [c5]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC. ICML 2024 - [i12]Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani:
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR abs/2402.03496 (2024) - [i11]Samarth Bhatia, Felix Dangel:
Lowering PyTorch's Memory Consumption for Selective Differentiation. CoRR abs/2404.12406 (2024) - [i10]Felix Dangel, Johannes Müller, Marius Zeinhofer:
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks. CoRR abs/2405.15603 (2024) - [i9]Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood:
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning. CoRR abs/2406.03276 (2024) - 2023
- [b1]Felix Dangel:
Backpropagation Beyond the Gradient. University of Tübingen, Germany, 2023 - [j1]Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. Trans. Mach. Learn. Res. 2023 (2023) - [c4]Agustinus Kristiadi, Felix Dangel, Philipp Hennig:
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. NeurIPS 2023 - [i8]Agustinus Kristiadi, Felix Dangel, Philipp Hennig
:
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR abs/2302.07384 (2023) - [i7]Felix Dangel:
Convolutions Through the Lens of Tensor Networks. CoRR abs/2307.02275 (2023) - [i6]Jonathan Wenger, Felix Dangel, Agustinus Kristiadi:
On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. CoRR abs/2310.00137 (2023) - [i5]Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani:
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR abs/2312.05705 (2023) - 2021
- [c3]Frank Schneider, Felix Dangel, Philipp Hennig:
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. NeurIPS 2021: 20825-20837 - [i4]Frank Schneider, Felix Dangel, Philipp Hennig:
Cockpit: A Practical Debugging Tool for Training Deep Neural Networks. CoRR abs/2102.06604 (2021) - [i3]Felix Dangel, Lukas Tatzel, Philipp Hennig:
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure. CoRR abs/2106.02624 (2021) - 2020
- [c2]Felix Dangel, Stefan Harmeling, Philipp Hennig:
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. AISTATS 2020: 799-808 - [c1]Felix Dangel, Frederik Kunstner, Philipp Hennig:
BackPACK: Packing more into Backprop. ICLR 2020
2010 – 2019
- 2019
- [i2]Felix Dangel, Philipp Hennig:
A Modular Approach to Block-diagonal Hessian Approximations for Second-order Optimization Methods. CoRR abs/1902.01813 (2019) - [i1]Felix Dangel, Frederik Kunstner, Philipp Hennig:
BackPACK: Packing more into backprop. CoRR abs/1912.10985 (2019)
Coauthor Index
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