Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Machine Learning and Knowledge Discovery in Databases |
| Untertitel | European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part V |
| Herausgeber/-innen | Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 344-360 |
| Seitenumfang | 17 |
| ISBN (elektronisch) | 978-3-031-26419-1 |
| ISBN (Print) | 9783031264184 |
| Publikationsstatus | Veröffentlicht - 17 März 2023 |
| Veranstaltung | 22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, Frankreich Dauer: 19 Sept. 2022 → 23 Sept. 2022 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 13717 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
This work presents an approach to optimize the weights of a discrete Hopfield network as mixed integer linear program (MILP). As the original formulation involves a sign-function, it is not differentiable, but parameter optimization using a (mixed integer) LP is possible. As autoassociative memory, a key question is the amount of patterns which can be stored in such a Hopfield network. In this work it is shown, that the traditional storage description models are far inferior to a globally optimized solution which can be obtained with a MILP. In contrast to a gradient descent based optimization is the proposed approach nearly parameter free and independent from seeding and other factors which are crucial for differentiable programming. Additionally it is possible to enforce sparsity constraints on the weights. Such additional constraints improve the generalization of such a model and make the Hopfield network more stable for the case of outliers or missing values. Several experiments demonstrate the effectiveness of the model.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part V. Hrsg. / Massih-Reza Amini; Stéphane Canu; Asja Fischer; Tias Guns; Petra Kralj Novak; Grigorios Tsoumakas. Springer Science and Business Media Deutschland GmbH, 2023. S. 344-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13717 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Mixed Integer Linear Programming for Optimizing a Hopfield Network
AU - Rosenhahn, Bodo
N1 - Funding Information: Acknowledgments. This work was supported by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor (grant no. 01DD20003), the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122) and the Erskine Programme at the University of Canterbury, New Zealand. The author also thanks Dr. Roberto Henschel for fruitful discussions and hints.
PY - 2023/3/17
Y1 - 2023/3/17
N2 - This work presents an approach to optimize the weights of a discrete Hopfield network as mixed integer linear program (MILP). As the original formulation involves a sign-function, it is not differentiable, but parameter optimization using a (mixed integer) LP is possible. As autoassociative memory, a key question is the amount of patterns which can be stored in such a Hopfield network. In this work it is shown, that the traditional storage description models are far inferior to a globally optimized solution which can be obtained with a MILP. In contrast to a gradient descent based optimization is the proposed approach nearly parameter free and independent from seeding and other factors which are crucial for differentiable programming. Additionally it is possible to enforce sparsity constraints on the weights. Such additional constraints improve the generalization of such a model and make the Hopfield network more stable for the case of outliers or missing values. Several experiments demonstrate the effectiveness of the model.
AB - This work presents an approach to optimize the weights of a discrete Hopfield network as mixed integer linear program (MILP). As the original formulation involves a sign-function, it is not differentiable, but parameter optimization using a (mixed integer) LP is possible. As autoassociative memory, a key question is the amount of patterns which can be stored in such a Hopfield network. In this work it is shown, that the traditional storage description models are far inferior to a globally optimized solution which can be obtained with a MILP. In contrast to a gradient descent based optimization is the proposed approach nearly parameter free and independent from seeding and other factors which are crucial for differentiable programming. Additionally it is possible to enforce sparsity constraints on the weights. Such additional constraints improve the generalization of such a model and make the Hopfield network more stable for the case of outliers or missing values. Several experiments demonstrate the effectiveness of the model.
KW - Hopfield network
KW - Mixed integer linear program
KW - Sparse models
UR - http://www.scopus.com/inward/record.url?scp=85151051689&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-26419-1_21
DO - 10.1007/978-3-031-26419-1_21
M3 - Conference contribution
AN - SCOPUS:85151051689
SN - 9783031264184
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 344
EP - 360
BT - Machine Learning and Knowledge Discovery in Databases
A2 - Amini, Massih-Reza
A2 - Canu, Stéphane
A2 - Fischer, Asja
A2 - Guns, Tias
A2 - Kralj Novak, Petra
A2 - Tsoumakas, Grigorios
PB - Springer Science and Business Media Deutschland GmbH
T2 - 22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022
Y2 - 19 September 2022 through 23 September 2022
ER -