Keywords
AccelerationConjugate gradient methodAlgorithmMathematicsApplied mathematicsComputer sciencePhysicsClassical mechanics
Related Publications
Long Short-Term Memory
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We brief...
<i>Ab initio</i>molecular dynamics for liquid metals
We present ab initio quantum-mechanical molecular-dynamics calculations based on the calculation of the electronic ground state and of the Hellmann-Feynman forces in the local-d...
Density Estimation for Statistics and Data Analysis
Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.
Elastic sheet method for identifying atoms in molecules
We have developed a new method for finding and representing dividing surfaces which can, for example, be used to identify “atoms” in molecules or condensed phases based on Bader...
Convergence Results for Neural Networks via Electrodynamics
We study whether a depth two neural network can learn another depth two network using gradient descent. Assuming a linear output node, we show that the question of whether gradi...
Publication Info
- Year
- 1993
- Type
- article
- Volume
- 88
- Issue
- 421
- Pages
- 221-221
- Citations
- 74
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
74
OpenAlex
Cite This
Mortaza Jamshidian,
Robert I. Jennrich
(1993).
Conjugate Gradient Acceleration of the EM Algorithm.
Journal of the American Statistical Association
, 88
(421)
, 221-221.
https://doi.org/10.2307/2290716
Identifiers
- DOI
- 10.2307/2290716