Segev Shlomov, Avi Yaeli
CHI 2024
We present methods to systematically design a feedforward neural-network detector from the knowledge of the channel characteristics. Its performance is compared with the conventional linear equalizer in a magnetic recording channel suffering from signal-dependent noise and nonlinear intersymbol interference. The superiority of the nonlinear schemes are clearly observed in all cases studied, especially in the presence of severe nonlinearity and noise. We also show that the decision boundaries formed by a theoretically derived neural-network classifier are geometrically close to those of a neural network trained by the backpropagation algorithm. The approach in this work is suitable for quantifying the gain in using a neural-network method as opposed to linear methods in the classification of noisy patterns. © 1997 IEEE.
Segev Shlomov, Avi Yaeli
CHI 2024
Arnold.L. Rosenberg
Journal of the ACM
Takuma Udagawa, Aashka Trivedi, et al.
EMNLP 2023
Ella Barkan, Ibrahim Siddiqui, et al.
Computational And Structural Biotechnology Journal