AIMC Topic: Learning

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Leveraging ResNet and label distribution in advanced intelligent systems for facial expression recognition.

Mathematical biosciences and engineering : MBE
With the development of AI (Artificial Intelligence), facial expression recognition (FER) is a hot topic in computer vision tasks. Many existing works employ a single label for FER. Therefore, the label distribution problem has not been considered fo...

Model predictive control for constrained robot manipulator visual servoing tuned by reinforcement learning.

Mathematical biosciences and engineering : MBE
For constrained image-based visual servoing (IBVS) of robot manipulators, a model predictive control (MPC) strategy tuned by reinforcement learning (RL) is proposed in this study. First, model predictive control is used to transform the image-based v...

Generation Learning Differences in Surgery: Why They Exist, Implication, and Future Directions.

The Surgical clinics of North America
The evolution of the knowledge economy and technology industry have fundamentally changed the learning environments occupied by contemporary surgical trainees and created pressures that will force the surgical community to consider. Although some lea...

NeuroAI: If grid cells are the answer, is path integration the question?

Current biology : CB
Spatially modulated neurons known as grid cells are thought to play an important role in spatial cognition. A new study has found that units with grid-cell-like properties can emerge within artificial neural networks trained to path integrate, and de...

Large Language Models and the Reverse Turing Test.

Neural computation
Large language models (LLMs) have been transformative. They are pretrained foundational models that are self-supervised and can be adapted with fine-tuning to a wide range of natural language tasks, each of which previously would have required a sepa...

Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism.

Briefings in bioinformatics
Graph neural networks based on deep learning methods have been extensively applied to the molecular property prediction because of its powerful feature learning ability and good performance. However, most of them are black boxes and cannot give the r...

Accelerating artificial intelligence: How federated learning can protect privacy, facilitate collaboration, and improve outcomes.

Health informatics journal
Cross-institution collaborations are constrained by data-sharing challenges. These challenges hamper innovation, particularly in artificial intelligence, where models require diverse data to ensure strong performance. Federated learning (FL) solves d...

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. However, these methods have some obvious limitations. On t...

A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working o...

Finding Hierarchical Structure in Binary Sequences: Evidence from Lindenmayer Grammar Learning.

Cognitive science
In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order infor...