Search Results for author:
Found 4 papers, 0 papers with code
Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions
Convolutional neural networks have shown impressive abilities in many applications, especially those related to the classification tasks.
Attention Enables Zero Approximation Error
In this work, we show that with suitable adaptations, the single-head self-attention transformer with a fixed number of transformer encoder blocks and free parameters is able to generate any desired polynomial of the input with no error.
Theory of Deep Convolutional Neural Networks II: Spherical Analysis
Deep learning based on deep neural networks of various structures and architectures has been powerful in many practical applications, but it lacks enough theoretical verifications.
Towards Understanding the Spectral Bias of Deep Learning
An intriguing phenomenon observed during training neural networks is the spectral bias, which states that neural networks are biased towards learning less complex functions.
