[图书][B] On priors for Bayesian neural networks
ET Nalisnick - 2018 - search.proquest.com
Deep neural networks have bested notable benchmarks across computer vision,
reinforcement learning, speech recognition, and natural language processing. However …
reinforcement learning, speech recognition, and natural language processing. However …
A hierarchical, data-driven approach to modeling single-cell populations predicts latent causes of cell-to-cell variability
All biological systems exhibit cell-to-cell variability. Frameworks exist for understanding how
stochastic fluctuations and transient differences in cell state contribute to experimentally …
stochastic fluctuations and transient differences in cell state contribute to experimentally …
Mineral exploration using modern data mining techniques
CT Barnett, PM Williams - 2005 - pubs.geoscienceworld.org
Returns from gold exploration have been disappointing over the last 20 years, despite the
surge in quality and quantity of exploration data. Historically, major discoveries have …
surge in quality and quantity of exploration data. Historically, major discoveries have …
Covariance prediction via convex optimization
We consider the problem of predicting the covariance of a zero mean Gaussian vector,
based on another feature vector. We describe a covariance predictor that has the form of a …
based on another feature vector. We describe a covariance predictor that has the form of a …
An Improved Taylor Algorithm for Computing the Matrix Logarithm
The most popular method for computing the matrix logarithm is a combination of the inverse
scaling and squaring method in conjunction with a Padé approximation, sometimes …
scaling and squaring method in conjunction with a Padé approximation, sometimes …
Computing the Matrix Logarithm with the Romberg Integration Method
The matrix logarithm function has applicability in many engineering and science fields.
Improvements in its calculation, from the point of view of both accuracy and/or execution …
Improvements in its calculation, from the point of view of both accuracy and/or execution …
Multivariate realized stock market volatility
We present a new matrix-logarithm model of the realized covariance matrix of stock returns.
The model uses latent factors which are functions of both lagged volatility and returns. The …
The model uses latent factors which are functions of both lagged volatility and returns. The …
Invariance priors for Bayesian feed-forward neural networks
U Toussaint, S Gori, V Dose - Neural Networks, 2006 - Elsevier
Neural networks (NN) are famous for their advantageous flexibility for problems when there
is insufficient knowledge to set up a proper model. On the other hand, this flexibility can …
is insufficient knowledge to set up a proper model. On the other hand, this flexibility can …
Polynomial approximations for the matrix logarithm with computation graphs
E Jarlebring, J Sastre, J González - arXiv preprint arXiv:2401.10089, 2024 - arxiv.org
The most popular method for computing the matrix logarithm is a combination of the inverse
scaling and squaring method in conjunction with a Pad\'e approximation, sometimes …
scaling and squaring method in conjunction with a Pad\'e approximation, sometimes …
[图书][B] Convex Optimization and Implicit Differentiation Methods for Control and Estimation
ST Barratt - 2021 - search.proquest.com
CONVEX OPTIMIZATION AND IMPLICIT DIFFERENTIATION METHODS FOR CONTROL AND
ESTIMATION A DISSERTATION SUBMITTED TO THE DEPARTMENT O Page 1 CONVEX …
ESTIMATION A DISSERTATION SUBMITTED TO THE DEPARTMENT O Page 1 CONVEX …