Update: my capstone project will be survey of the techniques and methodologies used in making deep learning models smaller and efficient so
that they can be run on the mobile platform. If time permits, I would also like to research about voice conversion using deep learning, especially
the recently published paper on the topic, Wavenet.
Advisor: David Barbella
Update: Annotated Bibliography
1. Deep learning on mobile
With the recent advances in deep learning and increase in the amount of data, we are now able to
create smarter applications with more accurate recognition engines. Most of the mobile applications
using deep learning only work online with the main processing being done in the cluster servers. But this
introduces unnecessarily delays and network bandwidth, with the additional disadvantage of not
working at all when the device is offline. Scaling down deep learning models into a mobile an interesting
area of research that could have impact on the future mobile applications. The power of the mobile
hardware will surely keep increasing, but there are quite a few software techniques we can use to
reduce the model size so that it can fit on an average mobile computing device.
More concretely, this research will analyze the current techniques used to reduce model size and
possibly offer possible future optimizations that can be done.
2. Voice conversion/morphing
Voice conversion is about conversing voice input from a person to the target’s voice signature. It used to
be very hard to replicate and convert to another person’s voice due to the sheer complexity of the task –
accounting for different accents and specific individual quirks. But it has theoretically become possible
to achieve a relatively effective conversion using neural networks. The practical applications are ample,
ranging from entertainment, giving unique voices to the disabled. Some security systems that use voice
recognition could even become obsolete.
3. Shopping Experience Enriched by Machine Learning
Recommendation engines are already a popular machine learning application used in e-commerce. In this research, I would like to experiment and research about the further applications of machine learning to enrich the user’s shopping experience. For example, we could train it to understand the reviews and summarize it instead of having the user read over the most recent reviews that may or may not be related to what they’re looking for. Also, the user can specify a problem statement (e.g. “I want to buy a present for my dad”), then the system could suggest possible gifts based on some prior training dataset.