There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The organization of behavior: A neuropsychological theory

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.

Deep learning with coherent nanophotonic circuits

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D2NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We created 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D2NNs.

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All-optical machine learning using diffractive deep neural networks

All-optical devices can exploit a suite of nonlinearities in silicon photonics. We study how microring resonators (MRRs)harness these nonlinearities, with theoretical model and experimental validation. Free-carrier effects will practically always dominate Kerr in MRRs. © The Authors(s)

Enhancing SOI Waveguide Nonlinearities via Microring Resonators

We present an integrated, multi-channel laser processor. It utilizes a novel photodetector-to-laser O/E/O receiverless link to receive multiple wavelength inputs. To our knowledge, this is the first laser neuron compatible with a wavelength-based networking scheme.

Demonstration of an O/E/O receiverless link in an integrated multi-channel laser neuron

Neuromorphic photonics promises orders of magnitude improvements in both speed and energy efficiency over digital electronics. We will give an overview of neuromorphic photonic systems and their application to optimization and machine learning problems. © 2019 The Author(s)

Multiwavelength Neuromorphic Photonics

Analog Interconnection networks are configured setting connection weights. Microring weight banks are a key device for making such networks in silicon photonic circuits. We demonstrate a small analog network in silicon using a single node with simple dynamics, showing dynamics parameterized by microring weight banks.

Silicon photonic weight bank control of integrated analog network dynamics

Reconfigurable photonic processors promise orders of magnitude improvements in both speed and energy efficiency over digital electronics for applications in neuromorphic computing and machine learning. We will provide an overview of neuromorphic photonic systems and their application to optimization and machine learning problems.

Thomas Ferreira de Lima

Papers

Enhancing SOI Waveguide Nonlinearities via Microring Resonators

Demonstration of an O/E/O receiverless link in an integrated multi-channel laser neuron

Multiwavelength Neuromorphic Photonics

Silicon photonic weight bank control of integrated analog network dynamics

TuD3.3 - Neuromorphic Photonic Processor Applications (Invited)