Schnaus, Dominik and Lee, Jongseok and Triebel, Rudolph (2021) Kronecker-Factored Optimal Curvature. In: Bayesian Deep Learning NeurIPS 2021 Workshop. Bayesian Deep Learning NeurIPS 2021 Workshop, 2021-12-13 - 2021-12-14, Virtual.
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Abstract
The current scalable Bayesian methods for Deep Neural Networks (DNNs) often rely on the Fisher Information Matrix (FIM). For the tractable computations of the FIM, the Kronecker-Factored Approximate Curvature (K-FAC) method is widely adopted, which approximates the true FIM by a layer-wise block-diagonal matrix, and each diagonal block is then Kronecker-factored. In this paper, we propose an alternative formulation to obtain the Kronecker-factored FIM. The key insight is to cast the given FIM computations into an optimization problem over the sums of Kronecker products. In particular, we prove that this formulation is equivalent to the best rank-one approximation problem, where the well-known power iteration method is guaranteed to converge to an optimal rank-one solution - resulting in our novel algorithm: the Kronecker-Factored Optimal Curvature (K-FOC). In a proof-of-concept experiment, we show that the proposed algorithm can achieve more accurate estimates of the true FIM when compared to the K-FAC method.
Item URL in elib: | https://elib.dlr.de/145806/ | ||||||||||||||||
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Document Type: | Conference or Workshop Item (Poster) | ||||||||||||||||
Title: | Kronecker-Factored Optimal Curvature | ||||||||||||||||
Authors: |
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Date: | 14 December 2021 | ||||||||||||||||
Journal or Publication Title: | Bayesian Deep Learning NeurIPS 2021 Workshop | ||||||||||||||||
Refereed publication: | Yes | ||||||||||||||||
Open Access: | Yes | ||||||||||||||||
Gold Open Access: | No | ||||||||||||||||
In SCOPUS: | No | ||||||||||||||||
In ISI Web of Science: | No | ||||||||||||||||
Status: | Published | ||||||||||||||||
Keywords: | Information theory, Deep Neural Networks, Fisher Information Matrix, Kronecker Factorization | ||||||||||||||||
Event Title: | Bayesian Deep Learning NeurIPS 2021 Workshop | ||||||||||||||||
Event Location: | Virtual | ||||||||||||||||
Event Type: | Workshop | ||||||||||||||||
Event Start Date: | 13 December 2021 | ||||||||||||||||
Event End Date: | 14 December 2021 | ||||||||||||||||
HGF - Research field: | Aeronautics, Space and Transport | ||||||||||||||||
HGF - Program: | Space | ||||||||||||||||
HGF - Program Themes: | Robotics | ||||||||||||||||
DLR - Research area: | Raumfahrt | ||||||||||||||||
DLR - Program: | R RO - Robotics | ||||||||||||||||
DLR - Research theme (Project): | R - Intelligent Mobility (RM) [RO] | ||||||||||||||||
Location: | Oberpfaffenhofen | ||||||||||||||||
Institutes and Institutions: | Institute of Robotics and Mechatronics (since 2013) Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition | ||||||||||||||||
Deposited By: | Lee, Jongseok | ||||||||||||||||
Deposited On: | 22 Nov 2021 17:33 | ||||||||||||||||
Last Modified: | 22 Jul 2024 13:45 |
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