Stochastic Model Predictive Control (SMPC) 
Mannequin predictive management (MPC) has demonstrated distinctive success for the high-performance management of advanced programs. The conceptual simplicity of MPC in addition to its capacity to successfully deal with the advanced dynamics of programs with a number of inputs and outputs, enter and state/output constraints, and conflicting management aims have made it a horny multivariable constrained management method. This text offers an outline of the principle developments within the space of stochastic mannequin predictive management (SMPC) up to now decade and supplies the reader with an impression of the totally different SMPC algorithms and the important thing theoretical challenges in stochastic predictive management with out undue mathematical complexity. The overall formulation of a stochastic OCP is first introduced, adopted by an outline of SMPC approaches for linear and nonlinear programs. Ideas of some avenues for future analysis on this quickly evolving subject concludes the article. …
Jazz
Jazz is a light-weight modular knowledge processing framework, together with an online server. It supplies knowledge persistence and computation capabilities accessible from R and Python and likewise by means of a REST API. …
Video Ladder Network (VLN)
We current the Video Ladder Community (VLN) for video prediction. VLN is a neural encoder-decoder mannequin augmented by each recurrent and feedforward lateral connections in any respect layers. The mannequin achieves aggressive outcomes on the Shifting MNIST dataset whereas having quite simple construction and offering quick inference. …
Low-Rank Principal Eigenmatrix Analysis
Sparse PCA is a broadly used approach for high-dimensional knowledge evaluation. On this paper, we suggest a brand new methodology referred to as low-rank principal eigenmatrix evaluation. Completely different from sparse PCA, the dominant eigenvectors are allowed to be dense however are assumed to have a low-rank construction when matricized appropriately. Such a construction arises naturally in a number of sensible circumstances: Certainly the highest eigenvector of a circulant matrix, when matricized appropriately is a rank-1 matrix. We suggest a matricized rank-truncated energy methodology that might be effectively carried out and set up its computational and statistical properties. In depth experiments on a number of artificial knowledge units display the aggressive empirical efficiency of our methodology. …
