Stochastic Processes and Applications

By Vladas Sidoravicius. 2007

358 citations recorded by the source corpus.

Subfields: Finance

View Paper: https://www.semanticscholar.org/paper/d26325b2236e3b0f0c3045f4dd5d747dc3be6676

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In Finance

1. Pratik Chaudhari
2. Stefano Soatto
3. Stanley J. Osher
4. Guillaume Carlier
5. Adam M. Oberman

Citations in the corpus

• Deep relaxation: partial differential equations for optimizing deep neural networks (2017, methodology): “In practice, “momentum” is often used to accelerate convergence to the invariant distribution; this corresponds to running the Langevin dynamics (Pavliotis, 2014, Chapter 6) given by In the overdamped limit c → ∞ we recover (SGD) and in the underdamped limit c = 0 we recover Hamiltonian dynamics.”
• Stochastic Gradient Descent Performs Variational Inference, Converges to Limit Cycles for Deep Networks (2017, methodology): “Markov Chain Monte Carlo: MCMC methods that sample from a negative log-likelihood Φ ( x ) have employed the idea of designing a force j = ∇Φ − ∇ f to accelerate convergence, see Ma et al. (2015) for a thorough survey, or Pavliotis (2016); Kaiser et al. (2017) for a rigorous treatment.”