Search Results for author:
Found 5 papers, 2 papers with code
A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks
To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate.
Comparison of neural closure models for discretised PDEs
Of the two trajectory fitting procedures, the discretise-then-optimise approach produces more accurate models than the optimise-then-discretise approach.
On the influence of stochastic roundoff errors and their bias on the convergence of the gradient descent method with low-precision floating-point computation
When implementing the gradient descent method in low precision, the employment of stochastic rounding schemes helps to prevent stagnation of convergence caused by the vanishing gradient effect.
A Simple and Efficient Stochastic Rounding Method for Training Neural Networks in Low Precision
Conventional stochastic rounding (CSR) is widely employed in the training of neural networks (NNs), showing promising training results even in low-precision computations.
Improved stochastic rounding
When a sequence of computations is implemented, round-off errors may be magnified or accumulated.
