A machine learning algorithm to improve building performance modeling during design.

Building design involves the optimization of factors affecting building performance such as building functions, comfort, safety, and energy. Building performance models (BPMs) help designers to evaluate and optimize such factors. However, the lack of design capabilities to validly describe human-building interactions for buildings under design may contribute to the development of inaccurate BPMs and the performance discrepancy between predictions and actual buildings.

Deep neural networks for texture classification-A theoretical analysis.

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks.