Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform.  show how CVNNs can be used to learn complex representations of real valued time-series data.  present methods and results using a framework that can compose holomorphic and non-holomorphic functions in a multi-layer network using a theoretical result called the Wirtinger derivative.  test our methods on a representation learning task for real-valued signals, recurrent complex-valued networks and their real-valued counterparts. Our results show that recurrent complex-valued networks can perform as ll as their real-valued counterparts while learning filters that are representative of the domain of the data.
['Andy M. Sarroff', 'Victor Shepardson', 'Michael A. Casey']
I am writing a research paper on a idea Learning Representations Using Complex-Valued Nets
[{"from": "human", "value": "I am writing a research paper on a idea Learning Representations Using Complex-Valued Nets"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform.  show how CVNNs can be used to learn complex representations of real valued time-series data.  present methods and results using a framework that can compose holomorphic and non-holomorphic functions in a multi-layer network using a theoretical result called the Wirtinger derivative.  test our methods on a representation learning task for real-valued signals, recurrent complex-valued networks and their real-valued counterparts. Our results show that recurrent complex-valued networks can perform as ll as their real-valued counterparts while learning filters that are representative of the domain of the data."}]