تقرير
Deep Learning with Parametric Lenses
| العنوان: | Deep Learning with Parametric Lenses |
|---|---|
| المؤلفون: | Cruttwell, Geoffrey S. H., Gavranovic, Bruno, Ghani, Neil, Wilson, Paul, Zanasi, Fabio |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Logic in Computer Science |
| الوصف: | We propose a categorical semantics for machine learning algorithms in terms of lenses, parametric maps, and reverse derivative categories. This foundation provides a powerful explanatory and unifying framework: it encompasses a variety of gradient descent algorithms such as ADAM, AdaGrad, and Nesterov momentum, as well as a variety of loss functions such as MSE and Softmax cross-entropy, and different architectures, shedding new light on their similarities and differences. Furthermore, our approach to learning has examples generalising beyond the familiar continuous domains (modelled in categories of smooth maps) and can be realised in the discrete setting of Boolean and polynomial circuits. We demonstrate the practical significance of our framework with an implementation in Python. Comment: arXiv admin note: text overlap with arXiv:2403.13001 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.00408 |
| رقم الأكسشن: | edsarx.2404.00408 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |