AIMC Topic: Neural Networks, Computer

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Learning to represent continuous variables in heterogeneous neural networks.

Cell reports
Animals must monitor continuous variables such as position or head direction. Manifold attractor networks-which enable a continuum of persistent neuronal states-provide a key framework to explain this monitoring ability. Neural networks with symmetri...

RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition.

Mathematical biosciences and engineering : MBE
Currently, identification of complex human activities is experiencing exponential growth through the use of deep learning algorithms. Conventional strategies for recognizing human activity generally rely on handcrafted characteristics from heuristic ...

Model-assisted deep learning of rare extreme events from partial observations.

Chaos (Woodbury, N.Y.)
To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data a...

Deep point cloud landmark localization for fringe projection profilometry.

Journal of the Optical Society of America. A, Optics, image science, and vision
Point clouds have been widely used due to their information being richer than images. Fringe projection profilometry (FPP) is one of the camera-based point cloud acquisition techniques that is being developed as a vision system for robotic surgery. F...

Extreme learning machine and genetic algorithm in quantitative analysis of sulfur hexafluoride by infrared spectroscopy.

Applied optics
Owing to the general disadvantages of traditional neural networks in gas concentration inversion, such as slow training speed, sensitive learning rate selection, unstable solutions, weak generalization ability, and an ability to easily fall into loca...

Quantitative endoscopic photoacoustic tomography using a convolutional neural network.

Applied optics
Endoscopic photoacoustic tomography (EPAT) is a catheter-based hybrid imaging modality capable of providing structural and functional information of biological luminal structures, such as coronary arterial vessels and the digestive tract. The recover...

Lightweight deep convolutional neural network for background sound classification in speech signals.

The Journal of the Acoustical Society of America
Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed wit...

Predicting the eigenstructures of metamaterials with QR-code meta-atoms by deep learning.

Optics letters
Deep neural networks (DNNs) facilitate the reverse design of metamaterial perfect absorbers (MPAs), usually by predicting the MPA structure from the input absorptivity. However, this suffers from the difficulty that the spectrum that actually exists ...

Image reconstruction with transformer for mask-based lensless imaging.

Optics letters
A mask-based lensless camera optically encodes the scene with a thin mask and reconstructs the image afterward. The improvement of image reconstruction is one of the most important subjects in lensless imaging. Conventional model-based reconstruction...

Orthogonality of diffractive deep neural network.

Optics letters
Some rules of the diffractive deep neural network (DNN) are discovered. They reveal that the inner product of any two optical fields in DNN is invariant and the DNN acts as a unitary transformation for optical fields. If the output intensities of the...