All Outputs (4)

High-dimensional function approximation using local linear embedding (2015)
Presentation / Conference Contribution
Andras, P. (2015). High-dimensional function approximation using local linear embedding. In 2015 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN.2015.7280370

Neural network approximation of high-dimensional nonlinear functions is difficult due to the sparsity of the data in the high-dimensional data space and the need for good coverage of the data space by the `receptive fields' of the neurons. However, h... Read More about High-dimensional function approximation using local linear embedding.

Detecting communities of methods using dynamic analysis data (2015)
Presentation / Conference Contribution
Andras, P., & Duffee, B. (2015). Detecting communities of methods using dynamic analysis data. In 2015 IEEE/ACM 6th International Workshop on Emerging Trends in Software Metrics. https://doi.org/10.1109/WETSoM.2015.11

Maintaining large-scale software is difficult due to the size and variable nature of such software. Network analysis is a promising approach to extract useful knowledge from network representations of large and complex systems. Community detection is... Read More about Detecting communities of methods using dynamic analysis data.

Environmental Factors and the Emergence of Cultural – Technical Innovations (2015)
Presentation / Conference Contribution
Andras, P. (2015). Environmental Factors and the Emergence of Cultural – Technical Innovations. In Proceedings of the European Conference on Artificial Life 2015 (130-137). https://doi.org/10.7551/978-0-262-33027-5-ch028

Environmental factors that determine ecological niches, for example natural boundaries formed by mountains, rivers, deserts, contribute to the speciation among animals. Similar factors have been proposed to be important for the... Read More about Environmental Factors and the Emergence of Cultural – Technical Innovations.

PD disease state assessment in naturalistic environments using deep learning (2015)
Presentation / Conference Contribution
Hammerla, N. Y., Fisher, J., Andras, P., Rochester, L., Walker, R., & Plötz, T. (2015). PD disease state assessment in naturalistic environments using deep learning. In AAAI'15: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence

Management of Parkinson's Disease (PD) could be improved significantly if reliable, objective information about fluctuations in disease severity can be obtained in ecologically valid surroundings such as the private home. Although automatic assessmen... Read More about PD disease state assessment in naturalistic environments using deep learning.