AIMC Topic: Neural Networks, Computer

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Hybrid autoencoder with orthogonal latent space for robust population structure inference.

Scientific reports
Analysis of population structure and genomic ancestry remains an important topic in human genetics and bioinformatics. Commonly used methods require high-quality genotype data to ensure accurate inference. However, in practice, laboratory artifacts a...

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies.

Traffic injury prevention
OBJECTIVE: The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomo...

An Extended Spatial Transformer Convolutional Neural Network for Gesture Recognition and Self-Calibration Based on Sparse sEMG Electrodes.

IEEE transactions on biomedical circuits and systems
sEMG-based gesture recognition is widely applied in human-machine interaction system by its unique advantages. However, the accuracy of recognition drops significantly as electrodes shift. Besides, in applications such as VR, virtual hands should be ...

Analog Gated Recurrent Unit Neural Network for Detecting Chewing Events.

IEEE transactions on biomedical circuits and systems
We present a novel gated recurrent neural network to detect when a person is chewing on food. We implemented the neural network as a custom analog integrated circuit in a 0.18 μm CMOS technology. The neural network was trained on 6.4 hours of data co...

Memristor Neural Network Circuit Based on Operant Conditioning With Immediacy and Satiety.

IEEE transactions on biomedical circuits and systems
Most of the operant conditioning only consider the basic theory, but the influencing factors such as immediacy and satiety are ignored. In this paper, a memristor neural network circuit based on operant conditioning with immediacy and satiety is prop...

A Co-Designed Neuromorphic Chip With Compact (17.9K F) and Weak Neuron Number-Dependent Neuron/Synapse Modules.

IEEE transactions on biomedical circuits and systems
Many efforts have been made to improve the neuron integration efficiency on neuromorphic chips, such as using emerging memory devices and shrinking CMOS technology nodes. However, in the fully connected (FC) neuromorphic core, increasing the number o...

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model.

Endoscopy
BACKGROUND: We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and to clinically validate the model through comparisons with DSOC expert and n...

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs.

Cell systems
We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data corre...

Feature flow regularization: Improving structured sparsity in deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Pruning is a model compression method that removes redundant parameters and accelerates the inference speed of deep neural networks (DNNs) while maintaining accuracy. Most available pruning methods impose various conditions on parameters or features ...

MixGradient: A gradient-based re-weighting scheme with mixup for imbalanced data streams.

Neural networks : the official journal of the International Neural Network Society
A challenge for contemporary deep neural networks in real-world problems is learning from an imbalanced data stream, where data tends to be received chunk by chunk over time, and the prior class distribution is severely imbalanced. Although many soph...