Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss approximation of functions using deep neural nets. Given a function  f  on a  d   -dimensional manifold   Γ⊂R m      Γ  ⊂    R    m         ,  construct a sparsely-connected depth-4 neural network and bound its error in approximating  f . The size of the network depends on dimension and curvature of the manifold Γ, the complexity of  f , in terms of its wavelet description, and only akly on the ambient dimension  m . Essentially, our network computes wavelet functions, which are computed from Rectified Linear Units (ReLU).
['Uri Shaham', 'Alexander Cloninger', 'Ronald R. Coifman']
I am writing a research paper on a idea Provable approximation properties for deep neural networks
[{"from": "human", "value": "I am writing a research paper on a idea Provable approximation properties for deep neural networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discuss approximation of functions using deep neural nets. Given a function  f  on a  d \u00a0 -dimensional manifold   \u0393\u2282R m      \u0393  \u2282    R    m         ,  construct a sparsely-connected depth-4 neural network and bound its error in approximating  f . The size of the network depends on dimension and curvature of the manifold \u0393, the complexity of  f , in terms of its wavelet description, and only akly on the ambient dimension  m . Essentially, our network computes wavelet functions, which are computed from Rectified Linear Units (ReLU)."}]