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Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation.
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The" wake-sleep" algorithm for unsupervised neural networks
Hinton, G. E., Dayan, P., Frey, B. J. & Neal, R. M · 1995
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Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type
Bi, G.-q. & Poo, M.-m · 1998
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Neuromorphic analog vlsi sensor for visual tracking: Circuits and application examples
Indiveri, G · 1999
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Competitive hebbian learning through spike-timing-dependent synaptic plasticity
Song, S., Miller, K. D. & Abbott, L. F · 2000
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
Jaeger, H · 2001
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Real-time computing without stable states: A new framework for neural computation based on perturbations
Maass, W., Natschläger, T. & Markram, H · 2002
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Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
Jaeger, H. & Haas, H · 2004
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On the computational power of circuits of spiking neurons
Maass, W. & Markram, H · 2004
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The organization of behavior: A neuropsychological theory (Psychology press, 2005)
Hebb, D. O · 2005
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Restricted boltzmann machines for collaborative filtering
Salakhutdinov, R., Mnih, A. & Hinton, G · 2007
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Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields
Matsushima, K. & Shimobaba, T · 2009
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion
Vincent, P. et al · 2010
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Neuromorphic silicon neuron circuits
Indiveri, G. et al · 2011
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Information processing using a single dynamical node as complex system
Appeltant, L. et al · 2011
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Exciton–polariton condensation
Keeling, J. & Berloff, N. G · 2011
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Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing
Larger, L. et al · 2012
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Optoelectronic reservoir computing
Paquot, Y. et al · 2012
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Memristor bridge synapse-based neural network and its learning
Adhikari, S. P., Yang, C., Kim, H. & Chua, L. O · 2012
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Parallel photonic information processing at gigabyte per second data rates using transient states
Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I · 2013
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A soft body as a reservoir: case studies in a dynamic model of octopus-inspired soft robotic arm
Nakajima, K. et al · 2013
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Quantum fluids of light
Carusotto, I. & Ciuti, C · 2013
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Self-configuring universal linear optical component
Miller, D. A · 2013
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Random feedback weights support learning in deep neural networks
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J · 2014
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Non-invasive on-chip light observation by contactless waveguide conductivity monitoring
Morichetti, F. et al · 2014
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Trainable hardware for dynamical computing using error backpropagation through physical media
Hermans, M., Burm, M., Van Vaerenbergh, T., Dambre, J. & Bienstman, P · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N. & Ganguli, S · 2015
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Direct feedback alignment provides learning in deep neural networks
Nøkland, A · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B. & Akerman, C. J · 2016
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The cma evolution strategy: A tutorial
Hansen, N · 2016
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Equilibrium propagation: Bridging the gap between energy-based models and backpropagation
Scellier, B. & Bengio, Y · 2017
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Deep learning with coherent nanophotonic circuits
Shen, Y. et al · 2017
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Harnessing disordered-ensemble quantum dynamics for machine learning
Fujii, K. & Nakajima, K · 2017
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Neuromorphic computing using non-volatile memory
Burr, G. W. et al · 2017
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Setting up meshes of interferometers - reversed local light interference method
Miller, D. A. B · 2017
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Attention is all you need
Vaswani, A. et al · 2017
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Deep reinforcement learning from human preferences
Christiano, P. F. et al · 2017
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Realizing the classical xy hamiltonian in polariton simulators
Berloff, N. G. et al · 2017
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Convolutional neural networks that teach microscopes how to image
Horstmeyer, R., Chen, R. Y., Kappes, B. & Judkewitz, B · 2017
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All-optical machine learning using diffractive deep neural networks
Lin, X. et al · 2018
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Vowel recognition with four coupled spin-torque nano-oscillators
Romera, M. et al · 2018
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Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification
Chang, J., Sitzmann, V., Dun, X., Heidrich, W. & Wetzstein, G · 2018
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Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R. & Bengio, Y · 2018
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Training of photonic neural networks through in situ backpropagation and gradient measurement
Hughes, T. W., Minkov, M., Shi, Y. & Fan, S · 2018
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Reinforcement learning in a large-scale photonic recurrent neural network
Bueno, J. et al · 2018
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End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging
Sitzmann, V. et al · 2018
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Recent advances in physical reservoir computing: A review
Tanaka, G. et al · 2019
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Reservoir computing with the frequency, phase, and amplitude of spin-torque nano-oscillators
Marković, D. et al · 2019
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Wave physics as an analog recurrent neural network
Hughes, T. W., Williamson, I. A., Minkov, M. & Fan, S · 2019
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Nanophotonic media for artificial neural inference
Khoram, E. et al · 2019
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Efficient convolutional neural network training with direct feedback alignment
Han, D. & Yoo, H.-j · 2019
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Putting an end to end-to-end: Gradient-isolated learning of representations
Löwe, S., O’Connor, P. & Veeling, B · 2019
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Training neural networks with local error signals
Nøkland, A. & Eidnes, L. H · 2019
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Memristor-based neural networks with weight simultaneous perturbation training
Wang, C., Xiong, L., Sun, J. & Yao, W · 2019
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Language models are unsupervised multitask learners
Radford, A. et al · 2019
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A. & Sutskever, I · 2019
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Parameter-efficient transfer learning for nlp
Houlsby, N. et al · 2019
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Large-scale optical neural networks based on photoelectric multiplication
Hamerly, R., Bernstein, L., Sludds, A., Soljačić, M. & Englund, D · 2019
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Photonic multiply-accumulate operations for neural networks
Nahmias, M. A. et al · 2019
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A fully integrated reprogrammable memristor–cmos system for efficient multiply–accumulate operations
Cai, F. et al · 2019
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Rajabalipanah, H., Abdolali, A., Shabanpour, J., Momeni, A. & Cheldavi, A · 2019
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In-memory computing on a photonic platform
Ríos, C. et al · 2019
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Inference in artificial intelligence with deep optics and photonics
Wetzstein, G. et al · 2020
Cited alongside, same era.
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R. & Eleftheriou, E · 2020
Cited alongside, same era.
Hardware beyond backpropagation: a photonic co-processor for direct feedback alignment
Launay, J. et al · 2020
Cited alongside, same era.
Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction
Rafayelyan, M., Dong, J., Tan, Y., Krzakala, F. & Gigan, S · 2020
Cited alongside, same era.
Polaritonic neuromorphic computing outperforms linear classifiers
Ballarini, D. et al · 2020
Cited alongside, same era.
Quantum neuromorphic computing
Marković, D. & Grollier, J · 2020
Intelligent meta-imagers: from compressed to learned sensing
Saigre-Tardif, C., Faqiri, R., Zhao, H., Li, L. & del Hougne, P · 2022
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Parallel temporal signal processing enabled by polarization-multiplexed programmable thz metasurfaces
Tahmasebi, O., Abdolali, A., Rajabalipanah, H., Momeni, A. & Fleury, R · 2022
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Parallel wave-based analog computing using metagratings
Rajabalipanah, H., Momeni, A., Rahmanzadeh, M., Abdolali, A. & Fleury, R · 2022
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To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects
Bai, B. et al · 2022
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Noise-adaptive intelligent programmable meta-imager
Qian, C. & del Hougne, P · 2022
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Delocalized photonic deep learning on the internet’s edge
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Cited alongside, same era.
Physical reservoir computing—an introductory perspective
Nakajima, K · 2020
Cited alongside, same era.
Neuromorphic metasurface
Wu, Z., Zhou, M., Khoram, E., Liu, B. & Yu, Z · 2020
Cited alongside, same era.
Accurate deep neural network inference using computational phase-change memory
Joshi, V. et al · 2020
Cited alongside, same era.
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media
Rahmani, B. et al · 2020
Cited alongside, same era.
Direct feedback alignment scales to modern deep learning tasks and architectures
Launay, J., Poli, I., Boniface, F. & Krzakala, F · 2020
Cited alongside, same era.
In situ optical backpropagation training of diffractive optical neural networks
Zhou, T. et al · 2020
Cited alongside, same era.
Sludds, A. et al · 2022
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Learning to compute with sound in nonlinear disordered cavities
Momeni, A., Guo, X., Lissek, H. & Fleury, R · 2022
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Experimentally realized in situ backpropagation for deep learning in photonic neural networks
Pai, S. et al · 2023
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Backpropagation-free training of deep physical neural networks
Momeni, A., Rahmani, B., Malléjac, M., del Hougne, P. & Fleury, R · 2023
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Forward–forward training of an optical neural network
Oguz, I. et al · 2023
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A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Le Gallo, M. et al · 2023
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Self-learning machines based on hamiltonian echo backpropagation
Lopez-Pastor, V. & Marquardt, F · 2023
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Fully non-linear neuromorphic computing with linear wave scattering
Wanjura, C. C. & Marquardt, F · 2023
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Deep photonic reservoir computer based on frequency multiplexing with fully analog connection between layers
Lupo, A., Picco, E., Zajnulina, M. & Massar, S · 2023
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Quantum reservoir computing with repeated measurements on superconducting devices
Yasuda, T. et al · 2023
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Overcoming the coherence time barrier in quantum machine learning on temporal data
Hu, F. et al · 2023
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Microwave signal processing using an analog quantum reservoir computer
Senanian, A. et al · 2023
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Beyond digital: Harnessing analog hardware for machine learning
Syed, M., Kalinin, K. & Berloff, N · 2023
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Thermodynamic ai and the fluctuation frontier
Coles, P. J. et al · 2023
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Bringing uncertainty quantification to the extreme-edge with memristor-based bayesian neural networks
Bonnet, D. et al · 2023
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Quantum-noise-limited optical neural networks operating at a few quanta per activation
Ma, S.-Y., Wang, T., Laydevant, J., Wright, L. G. & McMahon, P. L · 2023
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Deep learning with coherent vcsel neural networks
Chen, Z. et al · 2023
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Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Rasch, M. J. et al · 2023
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A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Gallo, M. L. et al · 2023
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The benefits of self-supervised learning for training physical neural networks
Laydevant, J., Lott, A., Venturelli, D. & McMahon, P. L · 2023
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Scaling forward gradient with local losses
Ren, M., Kornblith, S., Liao, R. & Hinton, G · 2023
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Blockwise self-supervised learning at scale
Siddiqui, S. A., Krueger, D., LeCun, Y. & Deny, S · 2023
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A cookbook of self-supervised learning
Balestriero, R. et al · 2023
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Phyff: Physical forward forward algorithm for in-hardware training and inference
Momeni, A., Rahmani, B., Malléjac, M., del Hougne, P. & Fleury, R · 2023
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High-performance real-world optical computing trained by in situ model-free optimization
Zhao, G., Shu, X. & Zhou, R · 2023
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Multiplexed gradient descent: Fast online training of modern datasets on hardware neural networks without backpropagation
McCaughan, A. N. et al · 2023
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Activity-difference training of deep neural networks using memristor crossbars
Yi, S.-i., Kendall, J. D., Williams, R. S. & Kumar, S · 2023
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Training neural networks with end-to-end optical backpropagation
Spall, J., Guo, X. & Lvovsky, A · 2023
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Nonlinear processing with linear optics
Yildirim, M., Dinc, N. U., Oguz, I., Psaltis, D. & Moser, C · 2023
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Deep learning with passive optical nonlinear mapping
Xia, F. et al · 2023
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Energy-based learning algorithms for analog computing: a comparative study
Scellier, B., Ernoult, M., Kendall, J. & Kumar, S · 2023
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Experimental demonstration of coupled learning in elastic networks
Altman, L. E., Stern, M., Liu, A. J. & Durian, D. J · 2023
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Machine learning without a processor: Emergent learning in a nonlinear electronic metamaterial
Dillavou, S. et al · 2023
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Contrastive learning through non-equilibrium memory
Falk, M., Strupp, A., Scellier, B. & Murugan, A · 2023
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Analog photonics computing for information processing, inference, and optimization
Stroev, N. & Berloff, N. G · 2023
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Llama: Open and efficient foundation language models. corr, abs/2302.13971, 2023. doi: 10.48550
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Palm: Scaling language modeling with pathways
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Mattergen: a generative model for inorganic materials design
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Mamba: Linear-time sequence modeling with selective state spaces
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Awq: Activation-aware weight quantization for llm compression and acceleration
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Bitnet: Scaling 1-bit transformers for large language models
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Efficient memory management for large language model serving with pagedattention (2023)
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Quantum neural network for quantum neural computing
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Physics-inspired neuroacoustic computing based on tunable nonlinear multiple-scattering
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Recent advances for quantum neural networks in generative learning
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Training deep boltzmann networks with sparse ising machines
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The physics of optical computing
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Image sensing with multilayer nonlinear optical neural networks
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Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor
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Learning diffractive optical communication around arbitrary opaque occlusions
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Reflectionless programmable signal routers
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Diffractive interconnects: all-optical permutation operation using diffractive networks
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