Deep learning with coherent nanophotonic circuits

TL;DR: This work introduces a real-time, web-based design tool that uses a trained deep neural network (DNN) for accurate far-field radiation prediction, which shows great potential and convenience for antenna and metasurface designs.

TL;DR: In this paper, it was shown that the Clements scheme can be realized using symmetric Mach-Zehnders, requiring only a small number of external phase-shifters that do not contribute to the depth of the circuit.

TL;DR: In this article , the authors report the development of a low-loss, high-bandwidth C-band silicon photonic platform on a 200 mm CMOS compatible process line, demonstrating wafer-level performance in the process.

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.