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Found 5 papers, 1 papers with code
The Probabilistic Stability of Stochastic Gradient Descent
A fundamental open problem in deep learning theory is how to define and understand the stability of stochastic gradient descent (SGD) close to a fixed point.
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
