Search Results for author:
Found 6 papers, 1 papers with code
An Explicit Scheme for Pathwise XVA Computations
Motivated by the equations of cross valuation adjustments (XVAs) in the realistic case where capital is deemed fungible as a source of funding for variation margin, we introduce a simulation/regression scheme for a class of anticipated BSDEs, where the coefficient entails a conditional expected shortfall of the martingale part of the solution.
The Probabilistic Stability of Stochastic Gradient Descent
Characterizing and understanding the stability of Stochastic Gradient Descent (SGD) remains an open problem in deep learning.
Exact Solutions of a Deep Linear Network
This work finds the analytical expression of the global minima of a deep linear network with weight decay and stochastic neurons, a fundamental model for understanding the landscape of neural networks.
SGD Can Converge to Local Maxima
Stochastic gradient descent (SGD) is widely used for the nonlinear, nonconvex problem of training deep neural networks, but its behavior remains poorly understood.
SGD with a Constant Large Learning Rate Can Converge to Local Maxima
Previous works on stochastic gradient descent (SGD) often focus on its success.
Multithreaded event-chain Monte Carlo with local times
We present a multithreaded event-chain Monte Carlo algorithm (ECMC) for hard spheres.
Computational Physics Soft Condensed Matter
