2.6k citations · 2.9k across the 5 of their papers we have counts for
5 papers
Dual adaptive training of photonic neural networks
Ziyang Zheng, Zhengyang Duan, Hang Chen +5
Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy ef…
Optical multi-task learning using multi-wavelength diffractive deep neural networks
Zhengyang Duan, Hang Chen, Xing Lin
Photonic neural networks are brain-inspired information processing technology using photons instead of electrons to perform artificial intelligence (AI) tasks. However, existing ar…
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
Deniz Mengu, Yi Luo, Yair Rivenson +3
In their Comment, Wei et al. (arXiv:1809.08360v1 [cs.LG]) claim that our original interpretation of Diffractive Deep Neural Networks (D2NN) represent a mischaracterization of the s…
All-Optical Machine Learning Using Diffractive Deep Neural Networks
Xing Lin, Yair Rivenson, Nezih T. Yardimci +3
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractiv…
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Yichen Wu, Yair Rivenson, Yibo Zhang +4
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s),…