D
Daniel D. Lee
Researcher at Samsung
Publications - 278
Citations - 36438
Daniel D. Lee is an academic researcher from Samsung. The author has contributed to research in topics: Robot & Artificial neural network. The author has an hindex of 45, co-authored 273 publications receiving 31726 citations. Previous affiliations of Daniel D. Lee include St. Michael's Hospital & Princess Margaret Cancer Centre.
Papers
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Proceedings Article
Algorithms for Non-negative Matrix Factorization
Daniel D. Lee,H. Sebastian Seung +1 more
TL;DR: Two different multiplicative algorithms for non-negative matrix factorization are analyzed and one algorithm can be shown to minimize the conventional least squares error while the other minimizes the generalized Kullback-Leibler divergence.
Journal ArticleDOI
Stan : A Probabilistic Programming Language
Bob Carpenter,Andrew Gelman,Matthew D. Hoffman,Daniel D. Lee,Ben Goodrich,Michael Betancourt,Marcus A. Brubaker,Jiqiang Guo,Peter Li,Allen Riddell +9 more
TL;DR: Stan as discussed by the authors is a probabilistic programming language for specifying statistical models, where a program imperatively defines a log probability function over parameters conditioned on specified data and constants, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration.
Stan: A Probabilistic Programming Language.
Bob Carpenter,Andrew Gelman,Matthew D. Hoffman,Daniel D. Lee,Ben Goodrich,Michael Betancourt,Marcus A. Brubaker,Jiqiang Guo,Peter Li,Allen Riddell +9 more
TL;DR: Stan is a probabilistic programming language for specifying statistical models that provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler and an adaptive form of Hamiltonian Monte Carlo sampling.
Journal ArticleDOI
The manifold ways of perception
H. Sebastian Seung,Daniel D. Lee +1 more
TL;DR: In an informative Perspective, Seung and Lee explain the mathematical intricacies of two new algorithms for modeling the variability of perceptual stimuli and other types of high-dimensional data.