AI Medical Compendium Journal:
Neural networks : the official journal of the International Neural Network Society

Showing 11 to 20 of 2842 articles

Enabling scale and rotation invariance in convolutional neural networks with retina like transformation.

Neural networks : the official journal of the International Neural Network Society
Traditional convolutional neural networks (CNNs) struggle with scale and rotation transformations, resulting in reduced performance on transformed images. Previous research focused on designing specific CNN modules to extract transformation-invariant...

Decomposition-based multi-scale transformer framework for time series anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Time series anomaly detection is crucial for maintaining stable systems. Existing methods face two main challenges. First, it is difficult to directly model the dependencies of diverse and complex patterns within the sequences. Second, many methods t...

Feature-Tuning Hierarchical Transformer via token communication and sample aggregation constraint for object re-identification.

Neural networks : the official journal of the International Neural Network Society
Recently, transformer-based methods have shown remarkable success in object re-identification. However, most works directly embed off-the-shelf transformer backbones for feature extraction. These methods treat all patch tokens equally, ignoring the d...

Zero-shot 3D anomaly detection via online voter mechanism.

Neural networks : the official journal of the International Neural Network Society
3D anomaly detection aims to solve the problem that image anomaly detection is greatly affected by lighting conditions. As commercial confidentiality and personal privacy become increasingly paramount, access to training samples is often restricted. ...

A shape composition method for named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Large language models (LLMs) roughly encode a sentence into a dense representation (a vector), which mixes up the semantic expression of all named entities within a sentence. So the decoding process is easily overwhelmed by sentence-specific informat...

Memristive circuit of emotion with negative feedback based on three primary color model.

Neural networks : the official journal of the International Neural Network Society
Many memristive circuits tend to oversimplify the process of emotion generation as a linear event, disregarding crucial factors such as negative feedback and other regulatory mechanisms. In this paper, a memristive circuit of emotion with negative fe...

Arch-Net: Model conversion and quantization for architecture agnostic model deployment.

Neural networks : the official journal of the International Neural Network Society
The significant computational demands of Deep Neural Networks (DNNs) present a major challenge for their practical application. Recently, many Application-Specific Integrated Circuit (ASIC) chips have incorporated dedicated hardware support for neura...

A semantic enhancement-based multimodal network model for extracting information from evidence lists.

Neural networks : the official journal of the International Neural Network Society
Courts require the extraction of crucial information about various cases from heterogeneous evidence lists for knowledge-driven decision-making. However, traditional manual screening is complex and inaccurate when confronted with massive evidence lis...

Event-based distributed cooperative neural learning control for nonlinear multiagent systems with time-varying output constraints.

Neural networks : the official journal of the International Neural Network Society
In practical engineering, many systems are required to operate under different constraint conditions due to considerations of system security. Violating these constraints conditions during operation may lead to performance degradation. Additionally, ...

Spatial and frequency information fusion transformer for image super-resolution.

Neural networks : the official journal of the International Neural Network Society
Previous works have indicated that Transformer-based models bring impressive image reconstruction performance in single image super-resolution (SISR). However, existing Transformer-based approaches utilize self-attention within non-overlapping window...