Felix Dangel

According to our database1, Felix Dangel authored at least 24 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator.
CoRR, July, 2025

Kronecker-factored Approximate Curvature (KFAC) From Scratch.
CoRR, July, 2025

Collapsing Taylor Mode Automatic Differentiation.
CoRR, May, 2025

Improving Energy Natural Gradient Descent through Woodbury, Momentum, and Randomization.
CoRR, May, 2025

Hide & Seek: Transformer Symmetries Obscure Sharpness & Riemannian Geometry Finds It.
CoRR, May, 2025

Spectral-factorized Positive-definite Curvature Learning for NN Training.
CoRR, February, 2025

Position: Curvature Matrices Should Be Democratized via Linear Operators.
CoRR, January, 2025

What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Lowering PyTorch's Memory Consumption for Selective Differentiation.
CoRR, 2024

Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Backpropagation Beyond the Gradient.
PhD thesis, 2023

ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure.
Trans. Mach. Learn. Res., 2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.
CoRR, 2023

On the Disconnect Between Theory and Practice of Overparametrized Neural Networks.
CoRR, 2023

Convolutions Through the Lens of Tensor Networks.
CoRR, 2023

The Geometry of Neural Nets' Parameter Spaces Under Reparametrization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
BackPACK: Packing more into Backprop.
Proceedings of the 8th International Conference on Learning Representations, 2020

Modular Block-diagonal Curvature Approximations for Feedforward Architectures.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
A Modular Approach to Block-diagonal Hessian Approximations for Second-order Optimization Methods.
CoRR, 2019


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