More about HKUST
Towards effective statistical-neural hybrid machine learning
PhD Thesis Proposal Defence Title: "Towards effective statistical-neural hybrid machine learning" by Mr. Yuchen YAN Abstract: We will propose two paradigms for closely integrating statistical and neural machine learning that make machine learning more efficient: (1) A statistical model and a neural network model should be able to co-train such that improvements to the statistical model can improve the neural network model and improvements to neural network model can improve the statistical model. In this way, the two models can form a feedback loop. (2) A neural network model should not just take the output of the statistical model as additional features. Instead, the neural network model can dynamically change its topology, following the statistical model's interpretation of the data. In this way, the neural network can utilize the graph/tree patterns recognized by the statistical model more naturally. We will also build a machine learning toolkit that is flexible enough to facilitate the implementations of our paradigms where existing toolkits have trouble dealing with. Date: Monday, 16 November 2020 Time: 2:00pm - 4:00pm Zoom Meeting: https://hkust.zoom.us/j/91228152449?pwd=QUZIMG9VQ2hDRWpPWWYvcllrdkpWQT09 Committee Members: Prof. Dekai Wu (Supervisor) Prof. Mordecai Golin (Chairperson) Prof. Dit-Yan Yeung Prof. Nevin Zhang **** ALL are Welcome ****