Invention Publication
- Patent Title: INTERPRETING CONVOLUTIONAL SEQUENCE MODEL BY LEARNING LOCAL AND RESOLUTION-CONTROLLABLE PROTOTYPES
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Application No.: US18479385Application Date: 2023-10-02
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Publication No.: US20240037397A1Publication Date: 2024-02-01
- Inventor: Jingchao Ni , Zhengzhang Chen , Wei Cheng , Bo Zong , Haifeng Chen
- Applicant: NEC Laboratories America, Inc.
- Applicant Address: US NJ Princeton
- Assignee: NEC Laboratories America, Inc.
- Current Assignee: NEC Laboratories America, Inc.
- Current Assignee Address: US NJ Princeton
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04

Abstract:
A method interprets a convolutional sequence model. The method converts an input data sequence having input segments into output features. The method clusters the input segments into clusters using respective resolution-controllable class prototypes allocated to each of classes. Each respective class prototype includes a respective output feature subset characterizing a respective associated class. The method calculates, using the clusters, similarity scores that indicate a similarity of an output feature to a respective class prototypes responsive to distances between the output feature and the respective class prototypes. The method concatenates the similarity scores to obtain a similarity vector. The method performs a prediction and prediction support operation that provides a value of prediction and an interpretation for the value responsive to the input segments and similarity vector. The interpretation for the value of prediction is provided using only non-negative weights and lacking a weight bias in the fully connected layer.
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