Time-multiplexed layer reuse for physical neural networks | Paper Citation Record

Observation d1bccea3-4b58-4179-9eff-46347e291523 · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Time-multiplexed layer reuse for physical neural networks Deep learning.nature, 521(7553):436–444, 2015

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Observation 4ee00410-b0d0-43a0-81bf-599f5a9b9a83 · outbound

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

Time-multiplexed layer reuse for physical neural networks Very Deep Convolutional Networks for Large-Scale Image Recognition

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Observation 6ccb9a85-9dae-4f88-b8ef-bf9dccb3b9d7 · outbound

This paper cites You only look once: Unified, real-time object detection.

Time-multiplexed layer reuse for physical neural networks You only look once: Unified, real-time object detection

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Observation be89b8f6-3711-4c4c-8256-60a0ae9334d1 · outbound

This paper cites Deep residual learning for image recognition.

Time-multiplexed layer reuse for physical neural networks Deep residual learning for image recognition

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Observation ab4a6ca6-9984-4fff-aeb3-4e5ef89dd159 · outbound

This paper cites Attention is all you need.

Time-multiplexed layer reuse for physical neural networks Attention is all you need

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Observation 07b29303-6cb5-4967-b892-bf5277f68885 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Time-multiplexed layer reuse for physical neural networks Bert: Pre-training of deep bidirectional transformers for language understanding

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Observation 16357a0f-7aa1-4b49-97dc-d3dff635ffa0 · outbound

This paper cites Inference in artificial intelligence with deep optics and photonics.Nature, 588(7836):39–47, 2020.

Time-multiplexed layer reuse for physical neural networks Inference in artificial intelligence with deep optics and photonics.Nature, 588(7836):39–47, 2020

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Observation 54d76234-70b6-4e6e-9dbd-4b7755038272 · outbound

This paper cites Information processing using a single dynamical node as complex system.

Time-multiplexed layer reuse for physical neural networks Information processing using a single dynamical node as complex system

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Observation 7d4a0d6e-195e-463f-9ac8-1b3e43029f35 · outbound

This paper cites Experimental demonstration of reservoir computing on a silicon photonics chip.Nature communications, 5(1):3541, 2014.

Time-multiplexed layer reuse for physical neural networks Experimental demonstration of reservoir computing on a silicon photonics chip.Nature communications, 5(1):3541, 2014

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Observation 34b3346b-e9fb-408d-bce2-f89965c05875 · outbound

This paper cites Deep learning with coherent nanophotonic circuits.Nature photonics, 11(7):441– 446, 2017.

Time-multiplexed layer reuse for physical neural networks Deep learning with coherent nanophotonic circuits.Nature photonics, 11(7):441– 446, 2017

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This paper cites Neuromorphic photonic networks using silicon photonic weight banks.Scientific reports, 7(1):7430, 2017.

Time-multiplexed layer reuse for physical neural networks Neuromorphic photonic networks using silicon photonic weight banks.Scientific reports, 7(1):7430, 2017

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Observation 3936268c-d197-41e2-8cd0-a3e72c8b6334 · outbound

This paper cites Reinforcement learning in a large-scale photonic recurrent neural network.Optica, 5(6):756–760, 2018.

Time-multiplexed layer reuse for physical neural networks Reinforcement learning in a large-scale photonic recurrent neural network.Optica, 5(6):756–760, 2018

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Observation 2080a32f-aae7-4339-9ddf-bccf4d58126c · outbound

This paper cites All-optical machine learning using diffractive deep neural networks.Science, 361(6406):1004–1008, 2018.

Time-multiplexed layer reuse for physical neural networks All-optical machine learning using diffractive deep neural networks.Science, 361(6406):1004–1008, 2018

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Observation 73a5b925-2225-4211-b718-a6d66d97b43b · outbound

This paper cites Silicon photonics for artificial intelligence acceleration: Hotchips.

Time-multiplexed layer reuse for physical neural networks Silicon photonics for artificial intelligence acceleration: Hotchips

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Observation 812d9110-1883-4fb6-a4ad-1b0de6834978 · outbound

This paper cites A crossbar array of magnetoresistive memory devices for in-memory computing.

Time-multiplexed layer reuse for physical neural networks A crossbar array of magnetoresistive memory devices for in-memory computing

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This paper cites Wright, Tatsuhiro Onodera, Martin M.

Time-multiplexed layer reuse for physical neural networks Wright, Tatsuhiro Onodera, Martin M

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This paper cites Neuromorphic computing with nanoscale spintronic oscillators.

Time-multiplexed layer reuse for physical neural networks Neuromorphic computing with nanoscale spintronic oscillators

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Observation e04df570-7f3c-4138-8aca-29273c5d635e · outbound

This paper cites Training a multilayer dynamical spintronic network with stan- dard machine-learning tools to perform time-series classification.Physical Review Applied, 23(3):034051, mar 2025.

Time-multiplexed layer reuse for physical neural networks Training a multilayer dynamical spintronic network with stan- dard machine-learning tools to perform time-series classification.Physical Review Applied, 23(3):034051, mar 2025

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This paper cites Vowel recognition with four coupled spin-torque nano- oscillators.Nature, 563(7730):230–234, 2018.

Time-multiplexed layer reuse for physical neural networks Vowel recognition with four coupled spin-torque nano- oscillators.Nature, 563(7730):230–234, 2018

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This paper cites A pro- grammable chemical computer with memory and pattern recognition.Nature communications, 11(1):1442, 2020.

Time-multiplexed layer reuse for physical neural networks A pro- grammable chemical computer with memory and pattern recognition.Nature communications, 11(1):1442, 2020

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Time-multiplexed layer reuse for physical neural networks Phys- ical implementation of reservoir computing through electrochemical reaction

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Time-multiplexed layer reuse for physical neural networks Imagenet classification with deep convolutional neural networks

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Time-multiplexed layer reuse for physical neural networks Improv- ing language understanding by generative pre-training

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Time-multiplexed layer reuse for physical neural networks Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

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This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Time-multiplexed layer reuse for physical neural networks Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

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Time-multiplexed layer reuse for physical neural networks Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

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Time-multiplexed layer reuse for physical neural networks Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

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Time-multiplexed layer reuse for physical neural networks Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23(120):1–39, 2022

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This paper cites Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023.

Time-multiplexed layer reuse for physical neural networks Palm: Scaling language modeling with pathways.Journal of Machine Learning Research, 24(240):1–113, 2023

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This paper cites All-optical spiking neurosynaptic networks with self- learning capabilities.Nature, 569(7755):208–214, 2019.

Time-multiplexed layer reuse for physical neural networks All-optical spiking neurosynaptic networks with self- learning capabilities.Nature, 569(7755):208–214, 2019

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Time-multiplexed layer reuse for physical neural networks Fully hardware-implemented memristor convolutional neural network.Nature, 577(7792):641–646, 2020

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Time-multiplexed layer reuse for physical neural networks IEEE Computer Society, 2020

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Time-multiplexed layer reuse for physical neural networks Novel nondelay-based reservoir computing with a single micromechanical nonlinear resonator for high-efficiency information processing.Microsystems & Nanoengineering, 7(1):83, 2021

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Time-multiplexed layer reuse for physical neural networks Collective and synchronous dynamics of photonic spiking neurons.Nature communications, 12(1):2325, 2021

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Time-multiplexed layer reuse for physical neural networks Scaling Laws for Neural Language Models

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Time-multiplexed layer reuse for physical neural networks Machine Learning Model Sizes and the Parameter Gap

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Time-multiplexed layer reuse for physical neural networks Diffractive optical computing in free space.Nature Communications, 15(1):1525, 2024

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Time-multiplexed layer reuse for physical neural networks Scaling up silicon photonic-based accelerators: Challenges and opportunities.APL Photonics, 7(2), 2022

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Time-multiplexed layer reuse for physical neural networks On the effect of the thermal cross-talk in a photonic feed-forward neural network based on silicon microresonators.Frontiers in Physics, 10:1093191, 2022

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Time-multiplexed layer reuse for physical neural networks Neuromorphic spintronics.Nature electronics, 3(7):360–370, 2020

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Time-multiplexed layer reuse for physical neural networks The physics of optical computing.Nature Reviews Physics, 5(12):717–734, 2023

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Time-multiplexed layer reuse for physical neural networks Handwritten digit recognition with a back-propagation network.Advances in neural information processing systems, 2, 1989

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Time-multiplexed layer reuse for physical neural networks Neural networks and principal component analysis: Learning from examples without local minima.Neural Networks, 2(1):53–58, 1989

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Time-multiplexed layer reuse for physical neural networks Balanced and deterministic weight-sharing helps network performance

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Time-multiplexed layer reuse for physical neural networks Quantum computing in the nisq era and beyond.Quantum, 2:79, 2018

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Time-multiplexed layer reuse for physical neural networks Unresolved cited work

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Time-multiplexed layer reuse for physical neural networks Behavioral classification of sequential neural activity using time varying recurrent neural networks.IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2025

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Time-multiplexed layer reuse for physical neural networks Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops.Nature communications, 12(1):5164, 2021

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Time-multiplexed layer reuse for physical neural networks HyperNetworks

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Time-multiplexed layer reuse for physical neural networks Wave physics as an analog recurrent neural network.Science advances, 5(12):eaay6946, 2019

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Time-multiplexed layer reuse for physical neural networks Dynamic filter networks.Advances in neural information processing systems, 29, 2016

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Time-multiplexed layer reuse for physical neural networks Using fast weights to attend to the recent past.Advances in neural information processing systems, 29, 2016

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Time-multiplexed layer reuse for physical neural networks Feedback control for microring weight banks.Optics express, 26(20):26422–26443, 2018

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Time-multiplexed layer reuse for physical neural networks Large-scale photonic processors 19 and their applications.npj Nanophotonics, 2(1):32, 2025

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Time-multiplexed layer reuse for physical neural networks Generating Sequences With Recurrent Neural Networks

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Time-multiplexed layer reuse for physical neural networks A Correspondence Between Random Neural Networks and Statistical Field Theory

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Observation 8c055431-4df9-4ac1-92bc-cdc86f64fc04 · outbound

This paper cites Digital light processing and mems: timely convergence for a bright future.Proceedings of SPIE, 2639:2–14, 1997.

Time-multiplexed layer reuse for physical neural networks Digital light processing and mems: timely convergence for a bright future.Proceedings of SPIE, 2639:2–14, 1997

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.298459Z

Source-reported events for the cited work

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Observation f4e1cb41-7583-4270-8211-e4c7255faf14 · outbound

This paper cites Femotosecond switching in a dual-core-fiber nonlinear coupler.Optics Letters, 13(10):904–906, 1988.

Time-multiplexed layer reuse for physical neural networks Femotosecond switching in a dual-core-fiber nonlinear coupler.Optics Letters, 13(10):904–906, 1988

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.347874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e673a9e5-9750-41e6-844e-4413fef81d92 · outbound

This paper cites Silicon microring resonators.Laser & photonics reviews, 6(1):47–73, 2012.

Time-multiplexed layer reuse for physical neural networks Silicon microring resonators.Laser & photonics reviews, 6(1):47–73, 2012

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.418216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d64b75f-40cb-4fd1-b035-063179bffff5 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

Time-multiplexed layer reuse for physical neural networks Reading digits in natural images with unsupervised feature learning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.503763Z

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Observation 038dea4c-2bc3-463f-88d3-822795b2b3df · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Time-multiplexed layer reuse for physical neural networks Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.565621Z

Source-reported events for the cited work

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Observation df1d967a-d024-4115-82f3-6962b4fc27d4 · outbound

This paper cites A theoretically grounded application of dropout in recurrent neural networks.Advances in neural information processing systems, 29, 2016.

Time-multiplexed layer reuse for physical neural networks A theoretically grounded application of dropout in recurrent neural networks.Advances in neural information processing systems, 29, 2016

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.622674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebef0d5a-614d-4e73-a87e-f2996e60e711 · outbound

This paper cites Shakespeare’s plays, sonnets and poems from the fol- ger shakespeare.

Time-multiplexed layer reuse for physical neural networks Shakespeare’s plays, sonnets and poems from the fol- ger shakespeare

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.695307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94ed04f4-1758-4ea7-99e0-9f3e88fe915e · outbound

This paper cites On the difficulty of training recurrent neural networks.

Time-multiplexed layer reuse for physical neural networks On the difficulty of training recurrent neural networks

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.771392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c5d189b-5f0a-4862-9df5-0851b251bf93 · outbound

This paper cites Learning long-term depen- dencies with gradient descent is difficult.IEEE transactions on neural networks, 5(2):157–166, 1994.

Time-multiplexed layer reuse for physical neural networks Learning long-term depen- dencies with gradient descent is difficult.IEEE transactions on neural networks, 5(2):157–166, 1994

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.838910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 995933fb-b282-4a8e-8dd2-0ce1eecdd18f · outbound

This paper cites van der Wiel.

Time-multiplexed layer reuse for physical neural networks van der Wiel

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.907538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de956ec2-805d-4d6d-93a5-923d0ddc7e03 · outbound

This paper cites Infor- mation processing capacity of dynamical systems.Scientific reports, 2(1):514, 2012.

Time-multiplexed layer reuse for physical neural networks Infor- mation processing capacity of dynamical systems.Scientific reports, 2(1):514, 2012

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:49.975442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e44f3f1a-fffc-4fc9-a383-554860cd3fa8 · outbound

This paper cites On the number of linear regions of deep neural networks.

Time-multiplexed layer reuse for physical neural networks On the number of linear regions of deep neural networks

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.030045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 259a67c0-3023-4a5c-8cdf-77ad369ded81 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Time-multiplexed layer reuse for physical neural networks Adam: A Method for Stochastic Optimization

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.116241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b83d81b4-29cb-43a0-9816-924c0df739e7 · outbound

This paper cites Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware.Nature Communications, 13(1):7847, 2022.

Time-multiplexed layer reuse for physical neural networks Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware.Nature Communications, 13(1):7847, 2022

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.178371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 039ab522-2445-4651-b986-efe39f9a8114 · outbound

This paper cites Blending optimal control and biologically plausible learning for noise-robust physical neural networks.Phys.

Time-multiplexed layer reuse for physical neural networks Blending optimal control and biologically plausible learning for noise-robust physical neural networks.Phys

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.232046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72d412b6-3e43-4e30-945f-8cd20ea2bb4b · outbound

This paper cites an unresolved cited work.

Time-multiplexed layer reuse for physical neural networks Unresolved cited work

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.310270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e24284f-a174-41cb-a08a-c0774d3dd77e · outbound

This paper cites Lecture 6e: Rmsprop — divide the gradient by a running average of its recent magnitude.

Time-multiplexed layer reuse for physical neural networks Lecture 6e: Rmsprop — divide the gradient by a running average of its recent magnitude

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.368922Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23dea27e-fe45-4af6-a79c-01492a401dcf · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278– 2324, 1998.

Time-multiplexed layer reuse for physical neural networks Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278– 2324, 1998

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.450142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f15e2f59-f013-4ed4-b47f-41055cc1cb5d · outbound

This paper cites Input (Hidden, Output).

Time-multiplexed layer reuse for physical neural networks Input (Hidden, Output)

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:50.503515Z

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