Efficient training and design of photonic neural network through neuroevolution | Paper Citation Record

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This paper cites an unresolved cited work.

Efficient training and design of photonic neural network through neuroevolution Unresolved cited work

Reference 1

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This paper cites 1(a), the network architecture of ANNs imitates the structure of biological neural network which includes a great number of neuron s and connections layer by layer [1].

Efficient training and design of photonic neural network through neuroevolution 1(a), the network architecture of ANNs imitates the structure of biological neural network which includes a great number of neuron s and connections layer by layer [1]

Reference 2

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This paper cites The iris plants dataset is a simple dataset which includes 150 instances (4 attributes for each instance).

Efficient training and design of photonic neural network through neuroevolution The iris plants dataset is a simple dataset which includes 150 instances (4 attributes for each instance)

Reference 3

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This paper cites Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,.

Efficient training and design of photonic neural network through neuroevolution Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups,

Reference 4

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This paper cites A review of unsupervised feature learning and deep learning for time-series modeling,.

Efficient training and design of photonic neural network through neuroevolution A review of unsupervised feature learning and deep learning for time-series modeling,

Reference 5

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This paper cites Deep learning,.

Efficient training and design of photonic neural network through neuroevolution Deep learning,

Reference 6

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This paper cites Deep learning in neural networks: An overview,.

Efficient training and design of photonic neural network through neuroevolution Deep learning in neural networks: An overview,

Reference 7

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This paper cites Recent trends in deep learning based natural language processing,.

Efficient training and design of photonic neural network through neuroevolution Recent trends in deep learning based natural language processing,

Reference 8

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This paper cites Imagenet classification with deep convolutional neural networks,.

Efficient training and design of photonic neural network through neuroevolution Imagenet classification with deep convolutional neural networks,

Reference 9

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Efficient training and design of photonic neural network through neuroevolution Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 10

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This paper cites End to End Learning for Self-Driving Cars.

Efficient training and design of photonic neural network through neuroevolution End to End Learning for Self-Driving Cars

Reference 11

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This paper cites Playing Atari with Deep Reinforcement Learning.

Efficient training and design of photonic neural network through neuroevolution Playing Atari with Deep Reinforcement Learning

Reference 12

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This paper cites Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,.

Efficient training and design of photonic neural network through neuroevolution Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates,

Reference 13

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This paper cites Theano: Deep learning on gpus with python,.

Efficient training and design of photonic neural network through neuroevolution Theano: Deep learning on gpus with python,

Reference 14

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This paper cites Optimizing fpga-based accelerator design for deep convolutional neural networks,.

Efficient training and design of photonic neural network through neuroevolution Optimizing fpga-based accelerator design for deep convolutional neural networks,

Reference 15

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This paper cites Going deeper with convolutions,.

Efficient training and design of photonic neural network through neuroevolution Going deeper with convolutions,

Reference 16

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This paper cites Deep residual learning for image recognition,.

Efficient training and design of photonic neural network through neuroevolution Deep residual learning for image recognition,

Reference 17

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This paper cites Long short-term memory,.

Efficient training and design of photonic neural network through neuroevolution Long short-term memory,

Reference 18

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This paper cites The spinnaker project,.

Efficient training and design of photonic neural network through neuroevolution The spinnaker project,

Reference 19

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This paper cites A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses andIn SituLearning,.

Efficient training and design of photonic neural network through neuroevolution A CMOS Spiking Neuron for Brain-Inspired Neural Networks With Resistive Synapses andIn SituLearning,

Reference 20

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This paper cites Diannao: A small-footprint high- throughput accelerator for ubiquitous machine-learning,.

Efficient training and design of photonic neural network through neuroevolution Diannao: A small-footprint high- throughput accelerator for ubiquitous machine-learning,

Reference 21

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This paper cites Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,.

Efficient training and design of photonic neural network through neuroevolution Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip,

Reference 22

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This paper cites Loihi: A neuromorphic manycore processor with on-chip learning,.

Efficient training and design of photonic neural network through neuroevolution Loihi: A neuromorphic manycore processor with on-chip learning,

Reference 23

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This paper cites Recent progress in semiconductor excitable lasers for photonic spike processing,.

Efficient training and design of photonic neural network through neuroevolution Recent progress in semiconductor excitable lasers for photonic spike processing,

Reference 24

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This paper cites As alternative approaches to gradient - based methods, evolutionary algorithms are representative gradient free methods to optimize the weights of ANNs [41, 42].

Efficient training and design of photonic neural network through neuroevolution As alternative approaches to gradient - based methods, evolutionary algorithms are representative gradient free methods to optimize the weights of ANNs [41, 42]

Reference 25

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This paper cites Optical computing,.

Efficient training and design of photonic neural network through neuroevolution Optical computing,

Reference 26

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This paper cites A leaky integrate-and-fire laser neuron for ultrafast cognitive computing,.

Efficient training and design of photonic neural network through neuroevolution A leaky integrate-and-fire laser neuron for ultrafast cognitive computing,

Reference 27

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This paper cites Multi-channel control for microring weight banks,.

Efficient training and design of photonic neural network through neuroevolution Multi-channel control for microring weight banks,

Reference 28

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This paper cites Variance preserving initialization for training deep neuromorphic photonic networks with sinusoidal activations,.

Efficient training and design of photonic neural network through neuroevolution Variance preserving initialization for training deep neuromorphic photonic networks with sinusoidal activations,

Reference 29

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This paper cites Deep learning with coherent nanophotonic circuits,.

Efficient training and design of photonic neural network through neuroevolution Deep learning with coherent nanophotonic circuits,

Reference 30

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This paper cites On-Chip Optical Convolutional Neural Networks.

Efficient training and design of photonic neural network through neuroevolution On-Chip Optical Convolutional Neural Networks

Reference 31

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Observation dfe9c7d8-ffd8-44ff-af0a-f2de43544134 · outbound

This paper cites Reinforcement learning in a large-scale photonic recurrent neural network,.

Efficient training and design of photonic neural network through neuroevolution Reinforcement learning in a large-scale photonic recurrent neural network,

Reference 32

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This paper cites All-optical machine learning using diffractive deep neural networks,.

Efficient training and design of photonic neural network through neuroevolution All-optical machine learning using diffractive deep neural networks,

Reference 33

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This paper cites Inverse design in nanophotonics,.

Efficient training and design of photonic neural network through neuroevolution Inverse design in nanophotonics,

Reference 34

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This paper cites An all-optical neuron with sigmoid activation function,.

Efficient training and design of photonic neural network through neuroevolution An all-optical neuron with sigmoid activation function,

Reference 35

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This paper cites Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks.

Efficient training and design of photonic neural network through neuroevolution Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks

Reference 36

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Observation db4ce5b6-f273-4450-bdf4-4a152e1383b2 · outbound

This paper cites Self-learning photonic signal processor with an optical neural network chip.

Efficient training and design of photonic neural network through neuroevolution Self-learning photonic signal processor with an optical neural network chip

Reference 37

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This paper cites Training of photonic neural networks through in situ backpropagation and gradient measurement,.

Efficient training and design of photonic neural network through neuroevolution Training of photonic neural networks through in situ backpropagation and gradient measurement,

Reference 38

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Observation f664fba2-5325-493d-8bf3-6ec0fdbbdaee · outbound

This paper cites Binary particle swarm optimized 2× 2 power splitters in a standard foundry silicon photonic platform,.

Efficient training and design of photonic neural network through neuroevolution Binary particle swarm optimized 2× 2 power splitters in a standard foundry silicon photonic platform,

Reference 39

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This paper cites Silicon photonics circuit design: methods, tools and challenges,.

Efficient training and design of photonic neural network through neuroevolution Silicon photonics circuit design: methods, tools and challenges,

Reference 40

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This paper cites Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,.

Efficient training and design of photonic neural network through neuroevolution Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks,

Reference 41

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This paper cites Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities,.

Efficient training and design of photonic neural network through neuroevolution Genetically optimized on-chip wideband ultracompact reflectors and Fabry–Perot cavities,

Reference 42

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This paper cites Optimization for Gold Nanostructure-Based Surface Plasmon Biosensors Using a Microgenetic Algorithm,.

Efficient training and design of photonic neural network through neuroevolution Optimization for Gold Nanostructure-Based Surface Plasmon Biosensors Using a Microgenetic Algorithm,

Reference 43

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Observation e7e29ae8-ff7b-4d28-a341-7de885050321 · outbound

This paper cites Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization,.

Efficient training and design of photonic neural network through neuroevolution Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization,

Reference 44

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This paper cites Designing neural networks through neuroevolution,.

Efficient training and design of photonic neural network through neuroevolution Designing neural networks through neuroevolution,

Reference 45

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Observation d90b5b6c-1a1e-4417-bb4a-57be99934546 · outbound

This paper cites Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.

Efficient training and design of photonic neural network through neuroevolution Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Reference 46

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This paper cites Rainbow: Combining improvements in deep reinforcement learning,.

Efficient training and design of photonic neural network through neuroevolution Rainbow: Combining improvements in deep reinforcement learning,

Reference 47

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This paper cites Deep learning in spiking neural networks,.

Efficient training and design of photonic neural network through neuroevolution Deep learning in spiking neural networks,

Reference 48

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This paper cites Optimal design for universal multiport interferometers,.

Efficient training and design of photonic neural network through neuroevolution Optimal design for universal multiport interferometers,

Reference 49

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This paper cites Deep learning with spiking neurons: opportunities and challenges,.

Efficient training and design of photonic neural network through neuroevolution Deep learning with spiking neurons: opportunities and challenges,

Reference 50

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Observation d71763a7-644f-4d2f-be09-91496a4b48c8 · outbound

This paper cites Nonlinear optics with 2D layered materials,.

Efficient training and design of photonic neural network through neuroevolution Nonlinear optics with 2D layered materials,

Reference 51

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Efficient training and design of photonic neural network through neuroevolution Unresolved cited work

Reference 52

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Observation e7105fc2-4102-4a2f-8efd-408fcced2760 · outbound

This paper cites Experimental realization of any discrete unitary operator,.

Efficient training and design of photonic neural network through neuroevolution Experimental realization of any discrete unitary operator,

Reference 53

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Observation 1af3c7b9-6b17-4413-975a-2bdbb81dc885 · outbound

This paper cites Feature selection based on hybridization of genetic algorithm and particle swarm optimization,.

Efficient training and design of photonic neural network through neuroevolution Feature selection based on hybridization of genetic algorithm and particle swarm optimization,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:18:33.413189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f6412510-a0e9-4838-86fa-4fcaeefe74f9 · outbound

This paper cites A study of cross-validation and bootstrap for accuracy estimation and model selection,.

Efficient training and design of photonic neural network through neuroevolution A study of cross-validation and bootstrap for accuracy estimation and model selection,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:18:33.292923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8ffa95ed-783e-4a33-b21e-d74cfd7f0f0c · outbound

This paper cites Maximum certainty data partitioning,.

Efficient training and design of photonic neural network through neuroevolution Maximum certainty data partitioning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:18:33.274424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 55724615-dce7-41c3-8ded-d4506ac55b40 · outbound

This paper cites Automatic identification of digital modulation types,.

Efficient training and design of photonic neural network through neuroevolution Automatic identification of digital modulation types,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:18:33.188249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 03b55f23-aba8-4f9d-bf7e-624aabd1ad01 · outbound

This paper cites This phenomenon is easy to explain because the large populations enhance the global searching ability of the evolution algorithms [36].

Efficient training and design of photonic neural network through neuroevolution This phenomenon is easy to explain because the large populations enhance the global searching ability of the evolution algorithms [36]

Reference 200

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:18:35.383292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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