تقرير
TREET: TRansfer Entropy Estimation via Transformer
| العنوان: | TREET: TRansfer Entropy Estimation via Transformer |
|---|---|
| المؤلفون: | Luxembourg, Omer, Tsur, Dor, Permuter, Haim |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Information Theory, Computer Science - Machine Learning |
| الوصف: | Transfer entropy (TE) is a measurement in information theory that reveals the directional flow of information between processes, providing valuable insights for a wide range of real-world applications. This work proposes Transfer Entropy Estimation via Transformers (TREET), a novel transformer-based approach for estimating the TE for stationary processes. The proposed approach employs Donsker-Vardhan (DV) representation to TE and leverages the attention mechanism for the task of neural estimation. We propose a detailed theoretical and empirical study of the TREET, comparing it to existing methods. To increase its applicability, we design an estimated TE optimization scheme that is motivated by the functional representation lemma. Afterwards, we take advantage of the joint optimization scheme to optimize the capacity of communication channels with memory, which is a canonical optimization problem in information theory, and show the memory capabilities of our estimator. Finally, we apply TREET to real-world feature analysis. Our work, applied with state-of-the-art deep learning methods, opens a new door for communication problems which are yet to be solved. Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.06919 |
| رقم الأكسشن: | edsarx.2402.06919 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |