Arman Melkumyan and Fabio Ramos. Multi-kernel gaussian processes. Proceed- ings of the Twenty-Second International Joint Conference on Artificial Intelli- gence, pages 1408-1413, 2011.A. Melkumyan and F. Ramos, "Multi-kernel gaussian processes," in Proceedings of the Twenty-Second international...
利用相邻 state-action 的空间相关性来加速学习,用 gaussian processes 建模 env dynamics(model-based)/ Q function(model-free),从而得到 model-based model-free 两种 MFRL 算法。算法结构跟 14 年的 MFRL 基本一致。全文没有数学证明。1 intro利用相邻 state-action 的空间相关性来加速学习:通过 Gaussian ...
):RD↦R defined over the functional space is placed with a GP prior f(x)∼GP(m(x),k(x,x′)),where m(x) is the mean function which usually takes zero without loss of generality, and k(x,x′) is the kernel (covariance) The LMC with neural embedding It is known that the ...
Tsuda, B. Schölkopf A Primer on Kernel Methods MIT Press, Cambridge, MA, USA (2004) pages 35–70 Google Scholar [32] J. Hensman, N. Fusi, N.D. Lawrence, Gaussian processes for big data, arXiv e-prints, page arXiv:1309.6835, September 2013. Google Scholar [33] E. Snelson, Z....
and constraints can regularize a machine learning approach in such a way that it can robustly learn from small and noisy data that evolve in time and space. Gaussian processes and neural networks have proven particularly powerful in this regard.43,44,45For Gaussian process regression, the partial...
This kernel function can be used to compute prior covariances between vector representations of materials, and by extension can be used to compute a prior covariance matrix among a set of materials. The posterior predicted means for the materials to be evaluated are then given by: $$\overright...
2. Create Gaussian kernel of width w i . Kernel elements should sum to unity. 3. Convolve image with kernel to create local mean image B⊗k w . 5. Calculate difference between image and the local mean image, square the difference, and convolve with kernel. Square-root the resulting ...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...
Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression. J. Afr. Earth Sci. 118, 53-64. https://doi.org/10.1016/j.jafrearsci.2016.02.019. [30] Dahal, R.K., Hasegawa, S., Nonomura, A., Yamanaka, M., Dhakal,...
given a kernel of a predictive Gaussian process regression (GPR) model and proper scaling parameters of the hyperrectangles,ϵwill be the maximum error of our Pareto front with probabilityδ(see Supplementary Note10)20. Setting a larger toleranceϵwill speed up the classification of the design...