（IS 19）On Learning Interpretable CNNs with Parametric Modulated Kernel-based Filters
會議：INTERSPEECH 2019 論文：On Learning Interpretable CNNs with Parametric Modulated Kernel-based Filters（基於參數調製的基於核的濾波器學習可解釋的CNN） 作者：Erfan Loweimi, Peter Bell, Steve Renals Abstract 我們研究了在卷積神經網絡（CNN）框架中使用
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相關文章
- 1. SOM-VAE: Interpretable Discrete Representation Learning on Time Series
- 2. Reading Note: Interpretable Convolutional Neural Networks
- 3. hand on machine learning with
- 4. Deep Learning Models on Kubernetes with GPUs
- 5. Interpretable Machine Learning線性迴歸
- 6. The 9 Deep Learning Papers You Need To Know About (Understanding CNNs Part 3)
- 7. #Paper Reading# TabNet: Attentive Interpretable Tabular Learning
- 8. [解讀] Unsupervised Feature Learning via Non-Parametric Instance Discrimination
- 9. Interpretable Models - Decision Rules
- 10. How Filters Work on Google Play