Maximum entropy approach for linear scaling in the electronic structure problem.
By David Alan Drabold, Sankey. 1993
Subfields: Computational Chemistry & Drug Discovery, AI for Physical Sciences
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Citations in the corpus
- The Full Spectrum of Deep Net Hessians At Scale: Dynamics with Sample Size (2018, neutral): “Our approach for approximating the Hessian of deep nets builds on the survey of [10], which discussed several different methods for approximating the density of the spectrum of large linear operators; many of these were first developed in the context of studies of large linear operators in quantum m”
- Estimating the Spectral Density of Large Implicit Matrices (2018, methodology): “For example, Drabold and Sankey (1993) used moment-constrained random vectors within the trace estimator to form estimates of the DOS based on the maximum entropy principle.”
- A probing method for computing the diagonal of a matrix inverse (2012, background): “It has also been exploited in a different form by physicists, who used this approach to compute the density of states, a function that describes the density of eigenvalues of an operator on the real interval, see, e.g. [8–10] and the many references therein which go back to the early 1970s.”
- The Full Spectrum of Deepnet Hessians at Scale: Dynamics with SGD Training and Sample Size. (2018, methodology): “r operators; many of which were first developed by physicists and chemists in quantum mechanics starting from the 1970’s [Ducastelle and Cyrot-Lackmann, 1970, Wheeler and Blumstein, 1972, Turek, 1988, Drabold and Sankey, 1993]. From the methods presented therein, we implemented and tested two: the La”
- Approximating Spectral Densities of Large Matrices (2013, unknown): “…density is such an important problem in quantum mechanics, there is an abundant literature devoted to this problem and research in this area was extremely active in the 1970s and 1980s, leading to clever and powerful methods developed by physicists and chemists [12, 42, 10, 44] for this purpose.”