OCIE: Augmenting model interpretability via Deconfounded Explanation-Guided Learning-Reference-Cited by-同舟云学术

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OCIE: Augmenting model interpretability via Deconfounded Explanation-Guided Learning

Published:2024-10 Issue: Volume:302 Page:112390
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Dong Liang^ORCID,Chen Leiyang,Zheng Chengliang,Fu Zhongwang,Zukaib Umer,Cui Xiaohui,Shen Zhidong^ORCID

Publisher

Elsevier BV

Reference79 articles.

1. Emotion recognition using three-dimensional feature and convolutional neural network from multichannel EEG signals;Chao;IEEE Sens. J.,2021

2. A deep interpretable representation learning method for speech emotion recognition;Jing;Inf. Process. Manage.,2023

3. Trustllm: Trustworthiness in large language models;Sun,2024

4. IFL-GAN: Improved federated learning generative adversarial network with maximum mean discrepancy model aggregation;Li;IEEE Trans. Neural Netw. Learn. Syst.,2023

5. Adversarial data hiding with only one pixel;Li;Inf. Process. Manage.,2023

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