Search Results for author:
Found 8 papers, 2 papers with code
Masked Vision-Language Transformers for Scene Text Recognition
MVLT is trained in two stages: in the first stage, we design a STR-tailored pretraining method based on a masking strategy; in the second stage, we fine-tune our model and adopt an iterative correction method to improve the performance.
Fetal Brain Tissue Annotation and Segmentation Challenge Results
Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context.
A deep learning method for solving stochastic optimal control problems driven by fully-coupled FBSDEs
In this paper, we mainly focus on the numerical solution of high-dimensional stochastic optimal control problem driven by fully-coupled forward-backward stochastic differential equations (FBSDEs in short) through deep learning.
A novel control method for solving high-dimensional Hamiltonian systems through deep neural networks
In this paper, we mainly focus on solving high-dimensional stochastic Hamiltonian systems with boundary condition, which is essentially a Forward Backward Stochastic Differential Equation (FBSDE in short), and propose a novel method from the view of the stochastic control.
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition
Deep neural networks have been shown as a class of useful tools for addressing signal recognition issues in recent years, especially for identifying the nonlinear feature structures of signals.
Solving stochastic optimal control problem via stochastic maximum principle with deep learning method
In this paper, we aim to solve the high dimensional stochastic optimal control problem from the view of the stochastic maximum principle via deep learning.
PMC-GANs: Generating Multi-Scale High-Quality Pedestrian with Multimodal Cascaded GANs
Recently, generative adversarial networks (GANs) have shown great advantages in synthesizing images, leading to a boost of explorations of using faked images to augment data.
Three algorithms for solving high-dimensional fully-coupled FBSDEs through deep learning
Recently, the deep learning method has been used for solving forward-backward stochastic differential equations (FBSDEs) and parabolic partial differential equations (PDEs).
