Volltext-Downloads (blau) und Frontdoor-Views (grau)
The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 47 of 3334
Back to Result List

GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks

  • Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by theCurrent training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.show moreshow less

Export metadata

Additional Services

Search Google Scholar

Statistics

frontdoor_oas
Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/3953
Bibliografische Angaben
Title (English):GradVis: Visualization and Second Order Analysis of Optimization Surfaces during the Training of Deep Neural Networks
Conference:IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), 18 November 2019, Denver, Colorado, USA
Author:Avraam Chatzimichailidis, Franz-Josef Pfreundt, Nicolas R. Gauger, Janis KeuperStaff MemberORCiDGND
Year of Publication:2019
Creating Corporation:IEEE
First Page:66
Last Page:74
Parent Title (English):Proceedings of MLHPC 2019: 5th Workshop on Machine Learning in HPC Environments
ISBN:978-1-7281-5985-0 (Online)
ISBN:978-1-7281-5986-7 (Print on Demand)
DOI:https://doi.org/10.1109/MLHPC49564.2019.00012
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Formale Angaben
Open Access: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt
Comment:
Conference held in conjunction with SC19:  The International Conference for High Performance Computing, Networking, Storage and Analysis, Denver, Colorado, November 17-22, 2019