[2G4-GS-2] Machine learning: pattern extraction

[2G4-GS-2-03] Visualization of the Prediction Basis of a Deep Learning Model using Query Learning Algorithms for Linear Patterns

〇Naoto Taketa¹, Tomoyuki Uchida¹, Takayoshi Shoudai², Satoshi MATSUMOTO³, Yusuke Suzuki¹, Tetsuhiro Miyahara¹ (1. Hiroshima City University, 2. Fukuoka Institute of Technology, 3. Tokai University)

Keywords:Deep Learning, Query Learning Algorithm, Linear Pattern

Based on the query learning model, which is one of the learning models in computational learning theory,
we propose a query learning algorithm visualizing the prediction basis of a trained Long Short Term Memory (LSTM) network M
whose training data is a set D of sequences consisting of constant symbols (constant strings).
In more detail, using a constant number of strings F in D, the prediction basis of a trained LSTM M is visualized as a linear pattern,
which is a sequence consisting of constant symbols and distinct variables, by repeating queries to M as an oracle O(n) times,
where n is the maximum length of strings in F.
In addition, for a set D of constant strings that match the synthetic linear pattern (target pattern),
we made a trained LSTM M with a subset of D as training data.
Then, in order to show effectiveness of the proposed algorithm, we report the linear pattern (pattern for visualization)
obtained by executing the proposed algorithm using M as an oracle.