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Found 7 papers, 0 papers with code
Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation.
CoRMF: Criticality-Ordered Recurrent Mean Field Ising Solver
We propose an RNN-based efficient Ising model solver, the Criticality-ordered Recurrent Mean Field (CoRMF), for forward Ising problems.
Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores
However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors.
Model-Free Prediction of Chaotic Systems Using High Efficient Next-generation Reservoir Computing
To predict the future evolution of dynamical systems purely from observations of the past data is of great potential application.
Identification of 27 abnormalities from multi-lead ECG signals: An ensembled Se-ResNet framework with Sign Loss function
Cardiovascular disease is a major threat to health and one of the primary causes of death globally.
A Comprehensive Survey of Grammar Error Correction
More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods.
