# Apply a state preprocessor with spec given by the "model" config option # (like other RL algorithms). This is mostly useful if you have a weird # observation shape, like an image. Auto-enabled if a custom model is set. "use_state_preprocessor": False, # Postprocess the policy netwo...
Gradient-based iterative algorithms for the tensor nearness problems associated with Sylvester tensor equations This paper is concerned with the solution of the tensor nearness problem associated with the Sylvester tensor equation represented by the Einstein product... ML Liang,B Zheng - 《Communications...
Convex Optimization in Signal Processing and Communications: Gradient-based algorithms with applications to signal-recovery problems Wu. Large sparse signal recovery by conju- gate gradient algorithm based on smoothing technique. Computers and Mathematics with Applications, 66(1):24-32,... A Beck,M ...
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. - microsoft/LightGBM
Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensi...
Thus, numerical algorithms for solving these problems are still attracting the attention of many researchers from various fields in science and engineering. Generally speaking, long-term hydrothermal scheduling can be defined as water releases from reservoirs for an interval, which ranges between one to...
Image restoration models based on total variation (TV) have become popular since their introduction by Rudin, Osher, and Fatemi (ROF) in 1992. The dual for... M Zhu,SJ Wright,TF Chan - 《Computational Optimization & Applications》 被引量: 257发表: 2010年 Duality-based algorithms for total...
In this paper, we show that gradient-based algorithms can be improved by a more careful allocation of privacy budget per iteration. Intuitively, at the beginning of the optimization, gradients are expected to be large, so that they do not need to be measured as accurately. However, as the ...
Algorithms gradient calculates the central difference for interior data points. For example, consider a matrix with unit-spaced data, A, that has horizontal gradient G = gradient(A). The interior gradient values, G(:,j), are G(:,j) = 0.5*(A(:,j+1) - A(:,j-1)); The subscript ...
[1] Bottou, Léon (1998).“Online Algorithms and Stochastic Approximations”. Online Learning and Neural Networks. Cambridge University Press. ISBN 978-0-521-65263-6 [2] Bottou, Léon.“Large-scale machine learning with stochastic gradient descent.”Proceedings of COMPSTAT’2010. Physica-Verlag HD...