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Found 8 papers, 2 papers with code
Boosting offline handwritten text recognition in historical documents with few labeled lines
In this paper, we face the problem of offline handwritten text recognition (HTR) in historical documents when few labeled samples are available and some of them contain errors in the train set.
The Generalized Complex Kernel Least-Mean-Square Algorithm
Also, the flexibility of the proposed generalized approach is tested in a second experiment with non-independent real and imaginary parts.
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
The current state-of-the-art inference method, Variational Inference (VI), employs a Gaussian approximation to the posterior distribution.
Boosting Handwriting Text Recognition in Small Databases with Transfer Learning
We first investigate, for a reduced and fixed number of training samples, 350 lines, how the learning from a large database, the IAM, can be transferred to the learning of the CLC of a reduced database, Washington.
Deep Gaussian Processes with Decoupled Inducing Inputs
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks.
Complex-Valued Kernel Methods for Regression
Usually, complex-valued RKHS are presented as an straightforward application of the real-valued case.
Proper Complex Gaussian Processes for Regression
A proper complex random variable or process is uncorrelated with its complex conjugate.
Gaussian Processes for Nonlinear Signal Processing
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing.
