Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence
By Akira Hirose, Shotaro Yoshida. IEEE Transactions on Neural Networks and Learning Systems, 2012
307 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/7c23c337d1e756b66a20f0915549e54a07dfbb46
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Mirco Ravanelli
- C. Pal
- Titouan Parcollet
- Dmitriy Serdyuk
- James R. Glass
- Sandeep Subramanian
- Mohit Bansal
In Signal Processing
- Yoshua Bengio
- Mirco Ravanelli
- Titouan Parcollet
- Dmitriy Serdyuk
- R. Mori
- C. Pal
- C. Trabelsi
- Georges Linarès
Citations in the corpus
- Quaternion Recurrent Neural Networks (2018, background): “More recently, neural networks of complex and hypercomplex numbers have received an increasing attention with motivations described in [7, 13, 35, 36], and some efforts have shown promising results in different applications.”
- Quaternion Convolutional Neural Networks (2018, background): “Complex-valued neural networks have been built and proved to have advantage on generalization ability [15] and can be more easily optimized [26].”
- Deep Complex Networks (2017, background): “From a computational point of view, it has been shown in Danihelka et al. [2016] that Holographic Reduced Representations [Plate, 2003], which use complex numbers, are numerically efficient and stable in the context of information retrieval from an associative memory.”