AIMC Topic: Learning

Clear Filters Showing 731 to 740 of 1476 articles

Intelligent career planning via stochastic subsampling reinforcement learning.

Scientific reports
Career planning consists of a series of decisions that will significantly impact one's life. However, current recommendation systems have serious limitations, including the lack of effective artificial intelligence algorithms for long-term career pla...

Feature blindness: A challenge for understanding and modelling visual object recognition.

PLoS computational biology
Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal tha...

Polyp segmentation network with hybrid channel-spatial attention and pyramid global context guided feature fusion.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In clinical practice, automatic polyp segmentation from colonoscopy images is an effective assistant manner in the early detection and prevention of colorectal cancer. This paper proposed a new deep model for accurate polyp segmentation based on an e...

CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.

Sensors (Basel, Switzerland)
Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with t...

A Feed-Forward Neural Network for Increasing the Hopfield-Network Storage Capacity.

International journal of neural systems
In the hippocampal dentate gyrus (DG), pattern separation mainly depends on the concepts of 'expansion recoding', meaning random mixing of different DG input channels. However, recent advances in neurophysiology have challenged the theory of pattern ...

Cascaded Parsing of Human-Object Interaction Recognition.

IEEE transactions on pattern analysis and machine intelligence
This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structural nature of the task, we introduce a cascaded parsing network (CP-HOI) for a multi-stage, structur...

Joint Feature Synthesis and Embedding: Adversarial Cross-Modal Retrieval Revisited.

IEEE transactions on pattern analysis and machine intelligence
Recently, generative adversarial network (GAN) has shown its strong ability on modeling data distribution via adversarial learning. Cross-modal GAN, which attempts to utilize the power of GAN to model the cross-modal joint distribution and to learn c...

Spatiotemporal neural network with attention mechanism for El Niño forecasts.

Scientific reports
To learn spatiotemporal representations and anomaly predictions from geophysical data, we propose STANet, a spatiotemporal neural network with a trainable attention mechanism, and apply it to El Niño predictions for long-lead forecasts. The STANet ma...

Brain-Inspired Experience Reinforcement Model for Bin Packing in Varying Environments.

IEEE transactions on neural networks and learning systems
Bin-packing problem (BPP) is a typical combinatorial optimization problem whose decision-making process is NP-hard. This article examines BPPs in varying environments, where random number and shape of items are to be packed in different instances. Th...

Triple-Memory Networks: A Brain-Inspired Method for Continual Learning.

IEEE transactions on neural networks and learning systems
Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters w...