The objective of this work is to improve the accuracy of building demand forecasting . This is a more challenging t ask than grid level forecasting. For the said purpose, we develop a new technique called recurrent transform learning (RTL). The first one (RTL) is unsupervised; this is used as a feature extraction tool that is further fed into a regression model. Forecasting experiments have been carried out on three popular publicly available datasets. Both of our proposed techniques yield results superior to the state - of - the - art like long short term memory network, echo state network and sparse coding regression. Index Terms -- demand forecasting, dynamical model, load forecasting, transform learning . H E impor tance of electrical load forecasting is well known. The issue has gained even more significance with the advent of smartgrids, microgrids and smart buildings. An excellent review on this topic can be found in [1].

In this paper, we consider the tensor completion problem representing the solution in the tensor train (TT) format. It is assumed that tensor is high-dimensional, and tensor values are generated by an unknown smooth function. The assumption allows us to develop an efficient initialization scheme based on Gaussian Process Regression and TT-cross approximation technique. The proposed approach can be used in conjunction with any optimization algorithm that is usually utilized in tensor completion problems. We empirically justify that in this case the reconstruction error improves compared to the tensor completion with random initialization. As an additional benefit, our technique automatically selects rank thanks to using the TT-cross approximation technique.

Graph-based Multi-view Binary Learning for Image Clustering Guangqi Jiang a, Huibing Wang a, Jinjia Peng a, Dongyan Chen a, Xianping Fu a,b, a College of Information and Science Technology, Dalian Maritime University, Danlian, Liaoning, 116021, China b Pengcheng Laboratory, Shenzhen, Guangdong, 518055, ChinaAbstract Hashing techniques, also known as binary code learning, have recently gained increasing attention in large-scale data analysis and storage. Generally, most existing hash clustering methods are single-view ones, which lack complete structure or complementary information from multiple views. For cluster tasks, abundant prior researches mainly focus on learning discrete hash code while few works take original data structure into consideration. To address these problems, we propose a novel binary code algorithm for clustering, which adopts graph embedding to preserve the original data structure, called (Graph-based Multi-view Binary Learning) GMBL in this paper. GMBL mainly focuses on encoding the information of multiple views into a compact binary code, which explores complementary information from multiple views. In particular, in order to maintain the graph-based structure of the original data, we adopt a Laplacian matrix to preserve the local linear relationship of the data and map it to the Hamming space. Considering different views have distinctive contributions to the final clustering results, GMBL adopts a strategy of automatically assign weights for each view to better guide the clustering. Finally, An alternating iterative optimization method is adopted to optimize discrete binary codes directly instead of relaxing the binary constraint in two steps. Experiments on five public datasets demonstrate the superiority of our proposed method compared with previous Corresponding author: Xianping Fu Preprint submitted to Journal of L A T EX Templates December 12, 2019 arXiv:1912.05159v1 Introduction With the development of computer vision applications, we have witnessed that hash technology has become an indispensable step in the processing of large data [1] [2]. In dealing with data analysis, organization, and storage, etc., there is an imminent need to use the effective hash code to process data clustering from big databases. Besides, most existed digital devices mainly based on binary code, which can effectively save computing time and storage space.

Various researchers have studied posterior inference of parameters in Bayesian mixture models [24, 42, 23], so that the statistical behavior of such models is relatively well-understood. In contrast, much less is known about the efficiency of different algorithms for sampling from the posterior distributions that arise from Bayesian mixture models. A standard approach for doing so is via some form of Markov Chain Monte Carlo (MCMC). Many different types of MCMC algorithms have been introduced for various types of Bayesian mixture models, including finite Bayesian mixture models [21, 49, 50, 26, 40], Dirichlet process mixture models [37, 41, 25, 28], and hierarchical and nested Dirichlet process models [52, 47]. Despite the plethora of possible MCMC methods, upper bounds on their mixing times are often challenging to establish. We refer the reader to the papers [27, 3, 55, 48, 57] for non-asymptotic upper bounds on mixing times for certain types of Bayesian models, different from those studied in this paper. In recent years, it has been increasingly common in the Bayesian literature to make use of a fractional likelihood--meaning an ordinary likelihood raised to some fractional power. Combining such a fractional likelihood with a prior distribution in the usual way leads to a class of posteriors known as power posterior or fractional posterior distributions. The power posterior distributions have been shown to have attractive properties in terms of robustness to mis-specification in Bayesian mixture models [39], and have been used in various applications 1 arXiv:1912.05153v1

Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test by an ensemble method that uses the sum of squared symmetry statistics and distance correlation. Simulation studies suggest that this method improves the power while preserving the clear interpretation of the binary expansion testing. We extend this method to tests of independence of random vectors in arbitrary dimension. By random projections, the proposed binary expansion randomized ensemble test transforms the multivariate independence testing problem into a univariate problem. Simulation studies and data example analyses show that the proposed method provides relatively robust performance compared with existing methods.

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model.

We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' opinions often differ greatly, making it difficult to predict their preferences from small amounts of personal data. Individual biases also make it harder to infer the consensus of a crowd when there are few labels per item. We address these challenges by combining matrix factorisation with Gaussian processes, using a Bayesian approach to account for uncertainty arising from noisy and sparse data. Our method exploits input features, such as text embeddings and user metadata, to predict preferences for new items and users that are not in the training set. As previous solutions based on Gaussian processes do not scale to large numbers of users, items or pairwise labels, we propose a stochastic variational inference approach that limits computational and memory costs. Our experiments on a recommendation task show that our method is competitive with previous approaches despite our scalable inference approximation. We demonstrate the method's scalability on a natural language processing task with thousands of users and items, and show improvements over the state of the art on this task. We make our software publicly available for future work.

Commonly-used clustering algorithms usually find ellipsoidal, spherical or other regular-structured clusters, but are more challenged when the underlying groups lack formal structure or definition. Syncytial clustering is the name that we introduce for methods that merge groups obtained from standard clustering algorithms in order to reveal complex group structure in the data. Here, we develop a distribution-free fully-automated syncytial clustering algorithm that can be used with $k$-means and other algorithms. Our approach computes the cumulative distribution function of the normed residuals from an appropriately fit $k$-groups model and calculates the nonparametric overlap between each pair of clusters. Groups with high pairwise overlap are merged as long as the generalized overlap decreases. Our methodology is always a top performer in identifying groups with regular and irregular structures in several datasets and can be applied to datasets with scatter or incomplete records. The approach is also used to identify the distinct kinds of gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and the distinct kinds of activation in a functional Magnetic Resonance Imaging study.

A BSTRACT Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes challenging for urban networks as their size and complexity are significantly higher compared to other networks. The state-of-the-art machine learning (ML) techniques allow us to detect hidden patterns and, thus, infer the features associated with them. However, very little is known about the impact on the performance of such predictive models by the use of different input representations. In this paper, we design and evaluate six different graph input representations (i.e., representations of the network paths), by considering the network's topological and temporal characteristics, for being used as inputs for machine learning models to learn the behavior of urban networks paths. The representations are validated and then tested with a real-world taxi journeys dataset predicting the tips using a road network of New Y ork. Our results demonstrate that the input representations that use temporal information help the model to achieve the highest accuracy (RMSE of 1.42$). K eywords Urban Networks, Graph Learning, Path Representation 1 Introduction Numerous important problems can be studied using the conceptual and theoretical framework of network science. Several structure and topological properties of networks have been widely studied in the recent years ([12, 14, 5, 9]). One of the most basic concepts in network science is the definition of network path ([3, 2]), i.e., a sequence of edges that joins a sequence of edges.

GMI-based end-to-end learning is shown to be highly nonconvex. We apply gradient descent initialized with Gray-labeled APSK constellations directly to the constellation coordinates. State-of-the-art constellations in 2D and 4D are found providing reach increases up to 26% w.r .t. to QAM. I NTRODUCTION S IGNAL shaping has recently received considerable attention in the literature and is now regarded as a key technique to improve throughput in high-speed fiberoptic systems. Shaping methods can be broadly categorized into probabilistic shaping (PS) and geometric shaping (GS), both having distinct advantages and disadvantages [1]-[3].