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Found 8 papers, 0 papers with code
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules.
End-To-End Graph-based Deep Semi-Supervised Learning
The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-based semi-supervised learning (SSL) approaches.
Modeling Historical AIS Data For Vessel Path Prediction: A Comprehensive Treatment
The prosperity of artificial intelligence has aroused intensive interests in intelligent/autonomous navigation, in which path prediction is a key functionality for decision supports, e. g. route planning, collision warning, and traffic regulation.
Semi-Supervised Deep Learning Using Improved Unsupervised Discriminant Projection
Deep learning demands a huge amount of well-labeled data to train the network parameters.
A Review of Semi Supervised Learning Theories and Recent Advances
Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning.
A Theoretical Study of The Relationship Between Whole An ELM Network and Its Subnetworks
A biological neural network is constituted by numerous subnetworks and modules with different functionalities.
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
In this paper, we propose a new graph-based $k$NN algorithm which can effectively handle both Gaussian distributed data and nonlinear manifold distributed data.
Mapping Temporal Variables into the NeuCube for Improved Pattern Recognition, Predictive Modelling and Understanding of Stream Data
This paper proposes a new method for an optimized mapping of temporal variables, describing a temporal stream data, into the recently proposed NeuCube spiking neural network architecture.
