Search Results for author:
Found 11 papers, 1 papers with code
Slimmed optical neural networks with multiplexed neuron sets and a corresponding backpropagation training algorithm
However, inter-channel crosstalk has obstructed WDM technologies to be deployed in nonlinear activation in ONNs.
EEG Opto-processor: epileptic seizure detection using diffractive photonic computing units
Here, we propose the EEG opto-processor based on diffractive photonic computing units (DPUs) to effectively process the extracranial and intracranial EEG signals and perform epileptic seizure detection.
Dual adaptive training of photonic neural networks
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 efficiency, and high parallelism.
Optical multi-task learning using multi-wavelength diffractive deep neural networks
By encoding multi-task inputs into multi-wavelength channels, the system can increase the computing throughput and significantly alle-viate the competition to perform multiple tasks in parallel with high accuracy.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
All-optical graph representation learning using integrated diffractive photonic computing units
Photonic neural networks perform brain-inspired computations using photons instead of electrons that can achieve substantially improved computing performance.
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons.
Response to Comment on "All-optical machine learning using diffractive deep neural networks"
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 system due to linearity and passivity.
All-Optical Machine Learning Using Diffractive Deep Neural Networks
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 diffractive layers that work collectively.
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording.
Transparent Object Reconstruction via Coded Transport of Intensity
Capturing and understanding visual signals is one of the core interests of computer vision.
