Fetching the paper…
Reading the bibliography…
Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency.
Floyd, R. W., & \& Steinberg, L. (1976). An Adaptive Algorithm for Spatial Greyscale. In Proceedings of the Society for Information Display
1976
Earlier work this paper cites.
LeCun, Y. (1998). The MNIST database of handwritten digits. http://yann. lecun. com/exdb/mnist/
1998
Earlier work this paper cites.
Agrawal, G.P. (2000). Nonlinear fiber optics. In Nonlinear Science at the Dawn of the 21st Century
2000
Earlier work this paper cites.
Kraskov, A., Stögbauer, H., & \& Grassberger, P. (2004). Estimating mutual information. Physical Review E
2004
Earlier work this paper cites.
Peng, H., Long, F., & \& Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence
2005
Earlier work this paper cites.
Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory
2006
Earlier work this paper cites.
Boyd, R. W., Gaeta, A. L., & \& Giese, E. (2008). Nonlinear optics. In Springer Handbook of Atomic, Molecular, and Optical Physics
2008
Earlier work this paper cites.
Dollár, P., Wojek, C., Schiele, B., & \& Perona, P. (2009). Pedestrian detection: A benchmark. In 2009 IEEE Conference on Computer Vision and Pattern Recognition
2009
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., & \& Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems
2012
Earlier work this paper cites.
Cover, T. M., & \& Thomas, J. A. (2012). Elements of information theory. Wiley
2012
Earlier work this paper cites.
Ohtsubo, J. (2013). Semiconductor Lasers Stability, Instability and Chaos Second, Enlarged Edition. SPRINGER SERIES IN OPTICAL SCIENCES
2013
Earlier work this paper cites.
Petterson, J., & \& Cukierski, W. (2013). Facial Keypoints Detection Dataset (provided by Dr. Yoshua Bengio of the University of Montreal). Kaggle. Retrieved from https://kaggle.com/competitions/facial-keypoints-detection
2013
Earlier work this paper cites.
Venkataraman, V., Saha, K., & \& Gaeta, A.L. (2013). Phase modulation at the few-photon level for weak-nonlinearity-based quantum computing. Nature Photonics
2013
Earlier work this paper cites.
Wu, B., Shastri, B.J., & \& Prucnal, P.R. (2014). Secure communication in fiber-optic networks. In Emerging trends in ICT security
2014
Earlier work this paper cites.
LeCun, Y., Bengio, Y., & \& Hinton, G. (2015). Deep learning. Nature
2015
Earlier work this paper cites.
Miller, D.A.B. (2015). Are optical transistors the logical next step? Nature Photonics
2015
Earlier work this paper cites.
Chen, C. L., Mahjoubfar, A., & \& Jalali, B. (2015). Optical data compression in time stretch imaging. PloS one
2015
Earlier work this paper cites.
Goodfellow, I., Bengio, Y., & \& Courville, A. (2016). Deep learning
2016
Earlier work this paper cites.
Shirdel, M., & \& Mansouri-Birjandi, M.A. (2016). Photonic crystal all-optical switch based on a nonlinear cavity. Optik
2016
Earlier work this paper cites.
Saade, A., Caltagirone, F., Carron, I., Daudet, L., Drémeau, A., Gigan, S., & \& Krzakala, F. (2016). Random projections through multiple optical scattering: Approximating kernels at the speed of light. In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2016
Earlier work this paper cites.
Wang, J., Zhu, B., Hao, Z., Bo, F., Wang, X., Gao, F., Li, Y., Zhang, G., & \& Xu, J. (2016). Thermo-optic effects in on-chip lithium niobate microdisk resonators. Optics Express
2016
Earlier work this paper cites.
Chen, T., & \& Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
2016
Cited alongside, same era.
Prucnal, Paul R., and Bhavin J. Shastri. Neuromorphic photonics. CRC press, 2017
2017
Cited alongside, same era.
Kues, M., Reimer, C., Roztocki, P., Cortes, L.R., Sciara, S., Wetzel, B., Zhang, Y., Cino, A., Chu, S.T., Little, B.E., et al. (2017). On-chip generation of high-dimensional entangled quantum states and their coherent control. Nature
2017
Cited alongside, same era.
Shen, Y., Harris, N.C., Skirlo, S., Prabhu, M., Baehr-Jones, T., Hochberg, M., Sun, X., Zhao, S., Larochelle, H., Englund, D., et al. (2017). Deep learning with coherent nanophotonic circuits. Nature Photonics
2017
Cited alongside, same era.
Teğin, U., Yıldırım, M., Oğuz, İ., Moser, C., & \& Psaltis, D. (2021). Scalable optical learning operator. Nature Computational Science
2021
Later among the works it cites.
Brossollet, C., Cappelli, A., Carron, I., Chaintoutis, C., Chatelain, A., Daudet, L., Gigan, S., Hesslow, D., Krzakala, F., Launay, J., & \& others. (2021). LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann co-processor. In 2021 IEEE Hot Chips 33 Symposium (HCS)
2021
Later among the works it cites.
Ohana, R., Medina, H., Launay, J., Cappelli, A., Poli, I., Ralaivola, L., & \& Rakotomamonjy, A. (2021). Photonic differential privacy with direct feedback alignment. Advances in Neural Information Processing Systems
2021
Later among the works it cites.
Ryou, A., Whitehead, J., Zhelyeznyakov, M., Anderson, P., Keskin, C., Bajcsy, M., & \& Majumdar, A. (2021). Free-space optical neural network based on thermal atomic nonlinearity. Photonics Research
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Tait, A.N., De Lima, T.F., Zhou, E., Wu, A.X., Nahmias, M.A., Shastri, B.J., & \& Prucnal, P.R. (2017). Neuromorphic photonic networks using silicon photonic weight banks. Scientific Reports
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Van der Sande, G., Brunner, D., & \& Soriano, M. C. (2017). Advances in photonic reservoir computing. Nanophotonics
2017
Cited alongside, same era.
Chang, J., Sitzmann, V., Dun, X., Heidrich, W., & \& Wetzstein, G. (2018). Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Scientific Reports
2018
Cited alongside, same era.
Hughes, T.W., Minkov, M., Shi, Y., & \& Fan, S. (2018). Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica
2018
Cited alongside, same era.
Lin, X., Rivenson, Y., Yardimci, N.T., Veli, M., Luo, Y., Jarrahi, M., & \& Ozcan, A. (2018). All-optical machine learning using diffractive deep neural networks. Science
2018
Cited alongside, same era.
Wang, M.M., Pagani, M., & \& Eggleton, B.J. (2018). A chip-integrated coherent photonic-phononic memory. Nature Communications
2018
Cited alongside, same era.
Porte, X., Skalli, A., Haghighi, N., Reitzenstein, S., Lott, J. A., & \& Brunner, D. (2021). A complete, parallel and autonomous photonic neural network in a semiconductor multimode laser. Journal of Physics: Photonics
2021
Later among the works it cites.
Li, J., Mengu, D., Yardimci, N.T., Luo, Y., Li, X., Veli, M., Rivenson, Y., Jarrahi, M., & \& Ozcan, A. (2021). Spectrally encoded single-pixel machine vision using diffractive networks. Science Advances
2021
Later among the works it cites.
Li, G.H.Y., Sekine, R., Nehra, R., Gray, R.M., Ledezma, L., Guo, Q., & \& Marandi, A. (2022). All-optical ultrafast ReLU function for energy-efficient nanophotonic deep learning. Nanophotonics
2022
Later among the works it cites.
Zhou, T., Scalzo, F., & \& Jalali, B. (2022). Nonlinear Schrödinger kernel for hardware acceleration of machine learning. Journal of Lightwave Technology
2022
Later among the works it cites.
Skalli, A., Robertson, J., Owen-Newns, D., Hejda, M., Porte, X., Reitzenstein, S., Hurtado, A., & \& Brunner, D. (2022). Photonic neuromorphic computing using vertical cavity semiconductor lasers. Optical Materials Express
2022
Later among the works it cites.
Wright, L. G., Onodera, T., Stein, M. M., Wang, T., Schachter, D. T., Hu, Z., & \& McMahon, P. L. (2022). Deep physical neural networks trained with backpropagation. Nature
2022
Later among the works it cites.
2022
Later among the works it cites.
Cappelli, A., Ohana, R., Launay, J., Meunier, L., Poli, I., & \& Krzakala, F. (2022). Adversarial robustness by design through analog computing and synthetic gradients. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2022
Later among the works it cites.
Wang, T., Sohoni, M.M., Wright, L.G., Stein, M.M., Ma, S.-Y., Onodera, T., Anderson, M.G., & \& McMahon, P.L. (2023). Image sensing with multilayer nonlinear optical neural networks. Nature Photonics
2023
Closest in time.
Eliezer, Y., Ruhrmair, U., Wisiol, N., Bittner, S., & \& Cao, H. (2023). Tunable nonlinear optical mapping in a multiple-scattering cavity. Proceedings of the National Academy of Sciences
2023
Closest in time.
Boikov, I. K., Brunner, D., & \& De Rossi, A. (2023). Evanescent coupling of nonlinear integrated cavities for all-optical reservoir computing. New Journal of Physics
2023
Closest in time.
Chen, Z., Sludds, A., Davis III, R., Christen, I., Bernstein, L., Ateshian, L., Heuser, T., Heermeier, N., Lott, J. A., Reitzenstein, S., & \& others. (2023). Deep learning with coherent VCSEL neural networks. Nature Photonics
2023
Closest in time.
2023
Closest in time.
Momeni, A., Rahmani, B., Malléjac, M., Del Hougne, P., & \& Fleury, R. (2023). Backpropagation-free training of deep physical neural networks. Science
2023
Closest in time.
Weng, X., Feng, J., Perry, A., & \& Vuong, L.T. (2023). Non-Line-of-Sight Full-Stokes Polarimetric Imaging with Solution-Processed Metagratings and Shallow Neural Networks. ACS Photonics
2023
Closest in time.
2023
Closest in time.
Yildirim, M., Dinc, N. U., Oguz, I., Psaltis, D., & \& Moser, C. (2024). Nonlinear Processing with Linear Optics. Nature Photonics
2024
Closest in time.