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Enhanced regularization for on-chip training using analog and temporary memory weights.

Neural networks : the official journal of the International Neural Network Society
In-memory computing techniques are used to accelerate artificial neural network (ANN) training and inference tasks. Memory technology and architectural innovations allow efficient matrix-vector multiplications, gradient calculations, and updates to n...

Evaluation of 5G and Fixed-Satellite Service Earth Station (FSS-ES) Downlink Interference Based on Artificial Neural Network Learning Models (ANN-LMS).

Sensors (Basel, Switzerland)
Fifth-generation (5G) networks have been deployed alongside fourth-generation networks in high-traffic areas. The most recent 5G mobile communication access technology includes mmWave and sub-6 GHz C-bands. However, 5G signals possibly interfere with...

Towards Adversarial Robustness for Multi-Mode Data through Metric Learning.

Sensors (Basel, Switzerland)
Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ...

Cross-Domain Indoor Visual Place Recognition for Mobile Robot via Generalization Using Style Augmentation.

Sensors (Basel, Switzerland)
The article presents an algorithm for the multi-domain visual recognition of an indoor place. It is based on a convolutional neural network and style randomization. The authors proposed a scene classification mechanism and improved the performance of...

Domain Adaptation Based on Semi-Supervised Cross-Domain Mean Discriminative Analysis and Kernel Transfer Extreme Learning Machine.

Sensors (Basel, Switzerland)
Good data feature representation and high precision classifiers are the key steps for pattern recognition. However, when the data distributions between testing samples and training samples do not match, the traditional feature extraction methods and ...

Network Security Situation Prediction Based on Optimized Clock-Cycle Recurrent Neural Network for Sensor-Enabled Networks.

Sensors (Basel, Switzerland)
We propose an optimized Clockwork Recurrent Neural Network (CW-RNN) based approach to address temporal dynamics and nonlinearity in network security situations, improving prediction accuracy and real-time performance. By leveraging the clock-cycle RN...

Discriminative ensemble meta-learning with co-regularization for rare fundus diseases diagnosis.

Medical image analysis
Deep neural networks (DNNs) have been widely applied in the medical image community, contributing to automatic ophthalmic screening systems for some common diseases. However, the incidence of fundus diseases patterns exhibits a typical long-tailed di...

A continuation method for image registration based on dynamic adaptive kernel.

Neural networks : the official journal of the International Neural Network Society
Image registration is a fundamental problem in computer vision and robotics. Recently, learning-based image registration methods have made great progress. However, these methods are sensitive to abnormal transformation and have insufficient robustnes...

Representational drift as a window into neural and behavioural plasticity.

Current opinion in neurobiology
Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, preceptsĀ and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural a...

A spatio-temporal network for video semantic segmentation in surgical videos.

International journal of computer assisted radiology and surgery
PURPOSE: Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Models need to provide accurate predictions since temporally inconsistent identification of anatomy can h...