Looking beyond LSTMs: Alternatives to Time Series Modelling using Neural Nets
Time series data, in today’s age, is ubiquitous. With the emerge of sensors, IOT devices it is spanning over all the modern aspects of life from basic household devices to self-driving cars affecting all for lives. Thus classification of time series is of unique importance in current time. With the advent of deep learning techniques , there have been influx of focus on Recurrent Neural Nets (RNN) in solving tasks related with sequence and rightly so. In this talk, I would attempt to describe the reason for success of RNN’s in sequence data. Eventually we would divert towards other techniques which should be looked into when working on such problems. I will phrase examples from healthcare domain and delve into some of the other usefull techniques that can be used from Deep Learning Domain and their usefullness.
Draft slides are included in the slides link. Mathematical and algorithmic details are removed for the talk to fit the material into a crisp format. Also attached is the link to our paper on related work.
Aditya Patel is the head of data science at Stasis and has 7+ years of experience spanning over the fields of Machine Learning and Signal Processing. He graduated with Dual Master’s degree in Biomedical and Electrical Engineering from University of Southern California. He has presented his work in Machine learning at multiple peer reviewed conferences concerning healthcare domain, across the geography. He also contributed to first generation “Artificial Pancreas” project in Medtronic, Los Angeles. In his current role he is leading the advent of smart hospitals in Indian healthcare.