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Deep neural networks have become a pervasive tool in science and engineering.
Intelligence without reason
Brooks, R. A · 1991
Earlier work this paper cites.
Noise and the reality gap: The use of simulation in evolutionary robotics
Jakobi, N., Husbands, P. & Harvey, I · 1995
Earlier work this paper cites.
An analytical MOS transistor model valid in all regions of operation and dedicated to low-voltage and low-current applications
Enz, C. C., Krummenacher, F. & Vittoz, E. A · 1995
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P · 1998
Earlier work this paper cites.
Nonlinear Fiber Optics (Elsevier, 2000)
Agrawal, G. P · 2000
Earlier work this paper cites.
An MOS transistor model for RF IC design valid in all regions of operation
Enz, C. C · 2002
Earlier work this paper cites.
Modelling and discretization of circuit problems
Günther, M., Feldmann, U. & ter Maten, J · 2005
Earlier work this paper cites.
Single-pixel imaging via compressive sampling
Duarte, M. F. et al · 2008
Earlier work this paper cites.
Nanometre-scale germanium photodetector enhanced by a near-infrared dipole antenna
Tang, L. et al · 2008
Earlier work this paper cites.
Description of ultrashort pulse propagation in multimode optical fibers
Poletti, F. & Horak, P · 2008
Earlier work this paper cites.
Single-pixel imaging via compressive sampling
Duarte, M. F. et al · 2008
Earlier work this paper cites.
Measuring the transmission matrix in optics: an approach to the study and control of light propagation in disordered media
Popoff, S. M. et al · 2010
Earlier work this paper cites.
A perspective on fast-spice simulation technology
Rewieński, M · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
Zeiler, M. D · 2012
Earlier work this paper cites.
Memristor crossbar-based neuromorphic computing system: A case study
Hu, M. et al · 2014
Earlier work this paper cites.
Properties of magnetic tunnel junctions with a MgO/CoFeB/Ta/CoFeB/MgO recording structure down to junction diameter of 11 nm
Sato, H. et al · 2014
Earlier work this paper cites.
CNN features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J. & Carlsson, S · 2014
Earlier work this paper cites.
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M. et al · 2015
Earlier work this paper cites.
Trainable hardware for dynamical computing using error backpropagation through physical media
Hermans, M., Burm, M., Van Vaerenbergh, T., Dambre, J. & Bienstman, P · 2015
Earlier work this paper cites.
Neural Networks and Deep Learning (Determination Press, 2015)
Nielsen, M · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2015
Earlier work this paper cites.
Single-chip microprocessor that communicates directly using light
Sun, C. et al · 2015
Earlier work this paper cites.
Femtojoule electro-optic modulation using a silicon–organic hybrid device
Koeber, S. et al · 2015
Earlier work this paper cites.
56 Gb/s germanium waveguide electro-absorption modulator
Srinivasan, S. A. et al · 2015
Earlier work this paper cites.
Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. & Belongie, S · 2016
Earlier work this paper cites.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J · 2016
Earlier work this paper cites.
Random projections through multiple optical scattering: Approximating kernels at the speed of light
Saade, A. et al · 2016
Earlier work this paper cites.
Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J · 2016
Earlier work this paper cites.
Residual networks behave like ensembles of relatively shallow networks
Veit, A., Wilber, M. & Belongie, S · 2016
Earlier work this paper cites.
A 23mW 24GS/s 6b time-interleaved hybrid two-step ADC in 28nm CMOS
Xu, B., Zhou, Y. & Chiu, Y · 2016
Earlier work this paper cites.
Photonic-crystal nano-photodetector with ultrasmall capacitance for on-chip light-to-voltage conversion without an amplifier
Nozaki, K. et al · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
Earlier work this paper cites.
Lets keep it simple, using simple architectures to outperform deeper and more complex architectures
Hasanpour, S. H., Rouhani, M., Fayyaz, M. & Sabokrou, M · 2016
Earlier work this paper cites.
Deep learning with coherent nanophotonic circuits
Shen, Y. et al · 2017
Cited alongside, same era.
Why does deep and cheap learning work so well?
Lin, H. W., Tegmark, M. & Rolnick, D · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y · 2017
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y · 2017
Cited alongside, same era.
Programmable controlled mode-locked fiber laser using a digital micromirror device
Liu, W. et al · 2017
Cited alongside, same era.
Neuromorphic computing with nanoscale spintronic oscillators
Torrejon, J. et al · 2017
Summarizing CPU and GPU design trends with product data
Sun, Y., Agostini, N. B., Dong, S. & Kaeli, D · 2019
Later among the works it cites.
Hooker, S · 2020
Later among the works it cites.
Survey of machine learning accelerators
Reuther, A. et al · 2020
Later among the works it cites.
Physics for neuromorphic computing
Marković, D., Mizrahi, A., Querlioz, D. & Grollier, J · 2020
Later among the works it cites.
Inference in artificial intelligence with deep optics and photonics
Wetzstein, G. et al · 2020
Later among the works it cites.
Neuromorphic metasurface
Wu, Z., Zhou, M., Khoram, E., Liu, B. & Yu, Z · 2020
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Cited alongside, same era.
Why does deep and cheap learning work so well?
Lin, H. W., Tegmark, M. & Rolnick, D · 2017
Cited alongside, same era.
Principal modes in multimode fibers: exploring the crossover from weak to strong mode coupling
Xiong, W. et al · 2017
Cited alongside, same era.
Efficient simulation of multimodal nonlinear propagation in step-index fibers
Lægsgaard, J · 2017
Cited alongside, same era.
Multimode nonlinear fiber optics: massively parallel numerical solver, tutorial, and outlook
Wright, L. G. et al · 2017
Cited alongside, same era.
A holistic fast and parallel approach for accurate transient simulations of analog circuits
Benk, J., Denk, G. & Waldherr, K · 2017
Cited alongside, same era.
Vowel recognition with four coupled spin-torque nano-oscillators
Romera, M. et al · 2018
Cited alongside, same era.
Later among the works it cites.
Neural Schrödinger equation: physical law as neural network
Nakajima, M., Tanaka, K. & Hashimoto, T · 2020
Later among the works it cites.
Classification with a disordered dopant-atom network in silicon
Chen, T. et al · 2020
Later among the works it cites.
A deep-learning approach to realizing functionality in nanoelectronic devices
Euler, H.-C. R. et al · 2020
Later among the works it cites.
Physical reservoir computing—an introductory perspective
Nakajima, K · 2020
Later among the works it cites.
Supervised learning in physical networks: From machine learning to learning machines
Stern, M., Hexner, D., Rocks, J. W. & Liu, A. J · 2020
Later among the works it cites.
Theoretical issues in deep networks
Poggio, T., Banburski, A. & Liao, Q · 2020
Later among the works it cites.
Near-sensor and in-sensor computing
Zhou, F. & Chai, Y · 2020
Later among the works it cites.
Neural sensors: Learning pixel exposures for hdr imaging and video compressive sensing with programmable sensors
Martel, J. N., Mueller, L. K., Carey, S. J., Dudek, P. & Wetzstein, G · 2020
Later among the works it cites.
Ultrafast machine vision with 2D material neural network image sensors
Mennel, L. et al · 2020
Later among the works it cites.
Quantum computational advantage using photons
Zhong, H.-S. et al · 2020
Later among the works it cites.
Scalable optical learning operator
Teğin, U., Yıldırım, M., Oğuz, İ., Moser, C. & Psaltis, D · 2020
Later among the works it cites.
ALP4lib: A Python wrapper for the Vialux ALP-4 controller suite to control DMDs, https://doi.org/10.5281/zenodo.4076193 (2020)
Popoff, S. M. & Matthès, M. W · 2020
Later among the works it cites.
History of supercomputing, https://en.wikipedia.org/wiki/History_of_supercomputing (2020)
2020
Later among the works it cites.
Green500 November 2020, https://www.top500.org/lists/green500/2020/11/ (2020)
2020
Later among the works it cites.
Ultrabroadband nonlinear optics in nanophotonic periodically poled lithium niobate waveguides
Jankowski, M. et al · 2020
Later among the works it cites.
Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction
Rafayelyan, M., Dong, J., Tan, Y., Krzakala, F. & Gigan, S · 2020
Later among the works it cites.
Scalable optical learning operator
Teğin, U., Yıldırım, M., Oğuz, İ., Moser, C. & Psaltis, D · 2020
Later among the works it cites.
Kernel computations from large-scale random features obtained by optical processing units
Ohana, R. et al · 2020
Later among the works it cites.
Up to two billion times acceleration of scientific simulations with deep neural architecture search
Kasim, M. et al · 2020
Later among the works it cites.
Carbon emissions and large neural network training
Patterson, D. et al · 2021
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Photonics for artificial intelligence and neuromorphic computing
Shastri, B. J. et al · 2021
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Physical deep learning based on optimal control of dynamical systems
Furuhata, G., Niiyama, T. & Sunada, S · 2021
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Quantum circuit optimization with deep reinforcement learning
Fösel, T., Niu, M. Y., Marquardt, F. & Li, L · 2021
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Self-learning machines based on hamiltonian echo backpropagation
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Reality-assisted evolution of soft robots through large-scale physical experimentation: a review
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Geforce 20 series - Wikipedia, https://en.wikipedia.org/wiki/GeForce_20_series (2021)
2021
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Reality-assisted evolution of soft robots through large-scale physical experimentation: a review
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