Understanding 1D Convolutional Neural Networks Using Multiclass Time-Varying Signals
Aug 10, 2018 publication description Tiger Prints – Master Research Thesis
In recent times, we have seen a surge in usage of Convolutional Neural Networks to solve all kinds of problems – from handwriting recognition to object recognition and from natural language processing to detecting exoplanets. Though the technology has been around for quite some time, there is still a lot of scope to do research on what’s really happening ’under the hood’ in a CNN model.
CNNs are considered to be black boxes which learn something from complex data and provides desired results. In this thesis, an effort has been made to explain what exactly CNNs are learning by training the network with carefully selected input data. The data considered here are one dimensional time varying signals and hence the 1-D convolutional neural networks are used to train, test and to analyze the learned weights.
The field of digital signal processing (DSP) gives a lot of insight into understanding the seemingly random weights learned by CNN. In particular, the concepts of Fourier transform, Savitzky-Golay filters, Guassian filters and FIR filter design lights up seeming dark alley of CNNs. As a result of this study, a few interesting inferences can be made regarding dropout regularization, optimal kernel length and optimal number of convolution layers.
Spatio-temproal prediction of crimes using network analytic approach (Pre-print)
arXiv.org
It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to cities. With the increase in urban population, it is important to keep an eye on criminal activities. By doing so, governments can enforce intelligent policing systems and hence many government agencies and local authorities have made the crime data publicly available. In this paper, we analyze Chicago city crime data fused with other social information sources using network analytic techniques to predict criminal activity for the next year. We observe that as we add more layers of data which represent different aspects of the society, the quality of prediction is improved. Our prediction models not just predict total number of crimes for the whole Chicago city, rather they predict number of crimes for all types of crimes and for different regions in City of Chicago.