arXiv subjects

Franz Rothlauf

Publications and source records attributed to Franz Rothlauf.

21 records · Page 2Linked to original sources

arXiv2015

Training a Restricted Boltzmann Machine for Classification by Labeling Model Samples

We propose an alternative method for training a classification model. Using the MNIST set of handwritten digits and Restricted Boltzmann Machines, it is possible to reach a classification performance competitive to semi-supervised learning if we first train a model in an unsupervised fashion on unlabeled data only, and then manually add labels to model samples instead of training data samples with the help of a GUI. This approach can benefit from the fact that model samples can be presented to the human labeler in a video-like fashion, resulting in a higher number of labeled examples. Also, after some initial training, hard-to-classify examples can be distinguished from easy ones automatically, saving manual work.

cs.LG↗

arXiv2014

Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and with problem complexity. The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA requires larger population sizes and a larger number of fitness evaluations, it outperforms BOA in terms of CPU times, in particular if the problem is large or complex. RBM-EDA requires less time for model building than BOA. These results highlight the potential of using generative neural networks for combinatorial optimization.

cs.NE↗

arXiv2014

Problem Complexity in Parallel Problem Solving

Recent works examine the relationship between the communication structure and the performance of a group in a problem solving task. Some conclude that inefficient communication networks with long paths outperform efficient networks on the long run. Others find no influence of the network topology on group performance. We contribute to this discussion by examining the role of problem complexity. In particular, we study whether and how the complexity of the problem at hand moderates the influence of the communication network on group performance. Results obtained from multi-agent modelling suggest that problem complexity indeed has an influence. We observe an influence of the network only for problems of moderate difficulty. For easier or harder problems, the influence of network topology becomes weaker or irrelevant, which offers a possible explanation for inconsistencies in the literature.

cs.SI↗