Deep learning with coherent nanophotonic circuits

TL;DR: It is shown that the fraction of transfer matrices non-implementable by the interferometers of the design diminishes rapidly with its size, and the design possesses superior resilience to manufacturing errors and losses without extra elements added into the scheme.

TL;DR: The integration of complex oxides on silicon has been a fast-moving area of research, where fundamental materials physics is in... as mentioned in this paper, where fundamental material physics is the main focus of research.

TL;DR: In this paper , an optical neural network (ONN) for photonic polarization qubit quantum state tomography (QST) has been proposed, which can determine the phase parameter of the qubit state accurately.

TL;DR: Researchers propose an on-chip optical matrix-vector multiplier using mode division multiplexing, reducing complexity and cost by eliminating multiple wavelengths and laser sources, and demonstrating 4-level modulation on a Silicon-On-Insulator platform.

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.

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.

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.

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.