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Found 6 papers, 1 papers with code
DocBinFormer: A Two-Level Transformer Network for Effective Document Image Binarization
Instead of using a simple vision transformer block to extract information from the image patches, the proposed architecture uses two transformer blocks for greater coverage of the extracted feature space on a global and local scale.
DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation
Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions.
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.
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
The merits of the proposed MCTL are four-fold: 1) the concept of manifold criterion (MC) is first proposed as a measure validating the distribution matching across domains, and domain adaptation is achieved if the MC is satisfied; 2) the proposed MC can well guide the generation of the intermediate domain sharing similar distribution with the target domain, by minimizing the local domain discrepancy; 3) a global generative discrepancy metric (GGDM) is presented, such that both the global and local discrepancy can be effectively and positively reduced; 4) a simplified version of MCTL called MCTL-S is presented under a perfect domain generation assumption for more generic learning scenario.
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.
Pulling back error to the hidden-node parameter technology: Single-hidden-layer feedforward network without output weight
SLFNs are universal approximators when at least the parameters of the networks including hidden-node parameter and output weight are exist.
