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Open AccessJournal ArticleDOI

Deep learning with coherent nanophotonic circuits

TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.
Abstract
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

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Citations
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Proceedings Article

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

TL;DR: This work presents a new architecture for implementing an Efficient Unitary Neural Network (EUNNs), and finds that this architecture significantly outperforms both other state-of-the-art unitary RNNs and the LSTM architecture, in terms of the final performance and/or the wall-clock training speed.
Journal ArticleDOI

Deep physical neural networks trained with backpropagation

TL;DR: Physical Neural Networks as discussed by the authors automatically train the functionality of any sequence of real physical systems, directly, using backpropagation, the same technique used for modern deep neural networks, using three diverse physical systems-optical, mechanical, and electrical.
Journal ArticleDOI

Large-Scale Photonic Ising Machine by Spatial Light Modulation.

TL;DR: In this article, a large-scale optical Ising machine with a spatial light modulator was designed and experimentally demonstrated, where the spin variables were encoded in a binary phase modulation of the field and the light propagation can be tailored to minimize an Ising Hamiltonian.
Journal ArticleDOI

Integrated lithium niobate photonics

TL;DR: In this paper, the basic structures including waveguides, cavities, periodically poled LiNbO3, and couplers, along with their fabrication methods and optical properties are reviewed.
Journal ArticleDOI

Artificial Sensory Memory.

TL;DR: increasing attention to this area would offer unprecedented opportunities toward new hardware architecture of artificial intelligence, which could extend the capabilities of digital systems with emotional/psychological attributes.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI

Human-level control through deep reinforcement learning

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI

Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI

Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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