تقرير
How Can Deep Neural Networks Fail Even With Global Optima?
| العنوان: | How Can Deep Neural Networks Fail Even With Global Optima? |
|---|---|
| المؤلفون: | Guan, Qingguang |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Numerical Analysis, 68T07, 65Z05, 65D99 |
| الوصف: | Fully connected deep neural networks are successfully applied to classification and function approximation problems. By minimizing the cost function, i.e., finding the proper weights and biases, models can be built for accurate predictions. The ideal optimization process can achieve global optima. However, do global optima always perform well? If not, how bad can it be? In this work, we aim to: 1) extend the expressive power of shallow neural networks to networks of any depth using a simple trick, 2) construct extremely overfitting deep neural networks that, despite having global optima, still fail to perform well on classification and function approximation problems. Different types of activation functions are considered, including ReLU, Parametric ReLU, and Sigmoid functions. Extensive theoretical analysis has been conducted, ranging from one-dimensional models to models of any dimensionality. Numerical results illustrate our theoretical findings. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.16872 |
| رقم الأكسشن: | edsarx.2407.16872 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |