AIMC Topic: Neural Networks, Computer

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Hydra: Multi-head low-rank adaptation for parameter efficient fine-tuning.

Neural networks : the official journal of the International Neural Network Society
The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks. Low-rank adaptation methods, such as LoRA, have gained significant attention due to their outsta...

Heterogeneous graph convolutional network for multi-view semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
This paper proposes a novel approach to semantic representation learning from multi-view datasets, distinct from most existing methodologies which typically handle single-view data individually, maintaining a shared semantic link across the multi-vie...

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologis...

A recurrent network model of planning explains hippocampal replay and human behavior.

Nature neuroscience
When faced with a novel situation, people often spend substantial periods of time contemplating possible futures. For such planning to be rational, the benefits to behavior must compensate for the time spent thinking. Here, we capture these features ...

CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.

Journal of chemical information and modeling
We present a novel and interpretable approach for assessing small-molecule binding using context explanation networks. Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to...

NmTHC: a hybrid error correction method based on a generative neural machine translation model with transfer learning.

BMC genomics
BACKGROUNDS: The single-pass long reads generated by third-generation sequencing technology exhibit a higher error rate. However, the circular consensus sequencing (CCS) produces shorter reads. Thus, it is effective to manage the error rate of long r...

Basketball technique action recognition using 3D convolutional neural networks.

Scientific reports
This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs), aiming to enhance the accurate and automated identification of various actions in b...

xECGArch: a trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features.

Scientific reports
Deep learning-based methods have demonstrated high classification performance in the detection of cardiovascular diseases from electrocardiograms (ECGs). However, their blackbox character and the associated lack of interpretability limit their clinic...

A fully automated classification of third molar development stages using deep learning.

Scientific reports
Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third mola...

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling.

Journal of neural engineering
. Spinal cord stimulation (SCS) is a well-established treatment for managing certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity to restore lost autonomic or sensory-motor function. ...