Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Neural conditional gradients

 
: Schramowski, Patrick; Bauckhage, Christian; Kersting, Kristian

:
Volltext ()

Online im WWW, 2018, arXiv:1803.04300, 10 S.
Englisch
Bericht, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
The move from hand-designed to learned optimizers in machine learning has been quite successful for gradient-based and -free optimizers. When facing a constrained problem, however, maintaining feasibility typically requires a projection step, which might be computationally expensive and not differentiable. We show how the design of projection-free convex optimization algorithms can be cast as a learning problem based on Frank-Wolfe Networks: recurrent networks implementing the Frank-Wolfe algorithm aka. conditional gradients. This allows them to learn to exploit structure when, e.g., optimizing over rank-1 matrices. Our LSTM-learned optimizers outperform hand-designed as well learned but unconstrained ones. We demonstrate this for training support vector machines and softmax classifiers.

: http://publica.fraunhofer.de/dokumente/N-506520.html