Jaccard Index  The Jaccard index, also known as the Jaccard similarity coefficient (originally coined coefficient de communauté by Paul Jaccard), is a statistic used for comparing the similarity and diversity of sample sets. The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets. 
Jack the Reader (Jack) 
Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (Jack), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. Jack is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse. 
Jackknife Regression  Jackknife logistic and linear regression for clustering and predictions. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameterfree, and usable and easytointerpret by nonstatisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. 
Jackknife Resampling  In statistics, the jackknife is a resampling technique especially useful for variance and bias estimation. The jackknife predates other common resampling methods such as the bootstrap. The jackknife estimator of a parameter is found by systematically leaving out each observation from a dataset and calculating the estimate and then finding the average of these calculations. Given a sample of size N, the jackknife estimate is found by aggregating the estimates of each N – 1 estimate in the sample. The jackknife technique was developed in Quenouille (1949, 1956). Tukey (1958) expanded on the technique and proposed the name “jackknife” since, like a Boy Scout’s jackknife, it is a “rough and ready” tool that can solve a variety of problems even though specific problems may be more efficiently solved with a purposedesigned tool. The jackknife represents a linear approximation of the bootstrap. 
JacSketch  We develop a new family of variance reduced stochastic gradient descent methods for minimizing the average of a very large number of smooth functions. Our method –JacSketch– is motivated by novel developments in randomized numerical linear algebra, and operates by maintaining a stochastic estimate of a Jacobian matrix composed of the gradients of individual functions. In each iteration, JacSketch efficiently updates the Jacobian matrix by first obtaining a random linear measurement of the true Jacobian through (cheap) sketching, and then projecting the previous estimate onto the solution space of a linear matrix equation whose solutions are consistent with the measurement. The Jacobian estimate is then used to compute a variancereduced unbiased estimator of the gradient. Our strategy is analogous to the way quasiNewton methods maintain an estimate of the Hessian, and hence our method can be seen as a stochastic quasigradient method. We prove that for smooth and strongly convex functions, JacSketch converges linearly with a meaningful rate dictated by a single convergence theorem which applies to general sketches. We also provide a refined convergence theorem which applies to a smaller class of sketches. This enables us to obtain sharper complexity results for variants of JacSketch with importance sampling. By specializing our general approach to specific sketching strategies, JacSketch reduces to the stochastic average gradient (SAGA) method, and several of its existing and many new minibatch, reduced memory, and importance sampling variants. Our rate for SAGA with importance sampling is the current bestknown rate for this method, resolving a conjecture by Schmidt et al (2015). The rates we obtain for minibatch SAGA are also superior to existing rates. 
JamesStein Estimator  The JamesStein estimator is a biased estimator of the mean of Gaussian random vectors. It can be shown that the JamesStein estimator dominates the ‘ordinary’ least squares approach, i.e., it has lower mean squared error on average. It is the bestknown example of Stein’s phenomenon. An earlier version of the estimator was developed by Charles Stein in 1956, and is sometimes referred to as Stein’s estimator. The result was improved by Willard James and Charles Stein in 1961. 
Jamovi  The jamovi project was founded to develop a free and open statistical platform which is intuitive to use, and can provide the latest developments in statistical methodology. At the core of the jamovi philosophy, is that scientific software should be ‘community driven’, where anyone can develop and publish analyses, and make them available to a wide audience. jamovi for R: Easy but Controversial 
JaroWinker Distance  In computer science and statistics, the JaroWinkler distance (Winkler, 1990) is a measure of similarity between two strings. It is a variant of the Jaro distance metric (Jaro, 1989, 1995), a type of string edit distance, and mainly used in the area of record linkage (duplicate detection). The higher the JaroWinkler distance for two strings is, the more similar the strings are. The JaroWinkler distance metric is designed and best suited for short strings such as person names. The score is normalized such that 0 equates to no similarity and 1 is an exact match. 
Java Class Library for Evolutionary Computation (JCLEC) 
JCLEC is a software system for Evolutionary Computation (EC) research, developed in the Java programming language. It provides a highlevel software framework to do any kind of Evolutionary Algorithm (EA), providing support for genetic algorithms (binary, integer and real encoding), genetic programming (Koza’s style, strongly typed, and grammar based) and evolutionary programming. 
Java Data Mining (JDM) 
Java Data Mining (JDM) is a standard Java API for developing data mining applications and tools. JDM defines an object model and Java API for data mining objects and processes. JDM enables applications to integrate data mining technology for developing predictive analytics applications and tools. The JDM 1.0 standard was developed under the Java Community Process as JSR 73. In 2006, the JDM 2.0 specification was being developed under JSR 247, but has been withdrawn in 2011 without standardization. Various data mining functions and techniques like statistical classification and association, regression analysis, data clustering, and attribute importance are covered by the 1.0 release of this standard. 
JAVA PMML (jpmml) 
jpmml, the world’s leading opensource PMML scoring engine to rapidly deploy predictive models into production. 
JavaScript 3D Library (three.js) 
The aim of the project is to create a lightweight 3D library with a very low level of complexity. The library provides <canvas>, <svg>, CSS3D and WebGL renderers. threejs 
JavaScript Object Notation (JSON) 
JSON, or JavaScript Object Notation, is an open standard format that uses humanreadable text to transmit data objects consisting of attributevalue pairs. It is used primarily to transmit data between a server and web application, as an alternative to XML. 
Jaya Optimisation Algorithm  An Efficient Multicore Implementation of the Jaya Optimisation Algorithm 
Jazz  Jazz is a lightweight modular data processing framework, including a web server. It provides data persistence and computation capabilities accessible from R and Python and also through a REST API. rjazz 
jblas  jblas is a fast linear algebra library for Java. jblas is based on BLAS and LAPACK, the defacto industry standard for matrix computations, and uses stateoftheart implementations like ATLAS for all its computational routines, making jBLAS very fast. jblas can is essentially a lightwight wrapper around the BLAS and LAPACK routines. These packages have originated in the Fortran community which explains their often archaic API. On the other hand modern implementations are hard to beat performance wise. jblas aims to make this functionality available to Java programmers such that they do not have to worry about writing JNI interfaces and calling conventions of Fortran code. jblas depends on an implementation of the LAPACK and BLAS routines. Currently it is tested with ATLAS (http://mathatlas.sourceforge.net ) and BLAS/LAPACK (http://…/lapack) 
JEDI  With the increasing demand for large amount of labeled data, crowdsourcing has been used in many largescale data mining applications. However, most existing works in crowdsourcing mainly focus on label inference and incentive design. In this paper, we address a different problem of adaptive crowd teaching, which is a subarea of machine teaching in the context of crowdsourcing. Compared with machines, human beings are extremely good at learning a specific target concept (e.g., classifying the images into given categories) and they can also easily transfer the learned concepts into similar learning tasks. Therefore, a more effective way of utilizing crowdsourcing is by supervising the crowd to label in the form of teaching. In order to perform the teaching and expertise estimation simultaneously, we propose an adaptive teaching framework named JEDI to construct the personalized optimal teaching set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that each learner has an exponentially decayed memory. Furthermore, it ensures comprehensiveness in the learning process by carefully balancing teaching diversity and learner’s accurate learning in terms of teaching usefulness. Finally, we validate the effectiveness and efficacy of JEDI teaching in comparison with the stateoftheart techniques on multiple data sets with both synthetic learners and real crowdsourcing workers. 
JeffreysLindley Paradox (JLP) 
Lindley’s paradox is a counterintuitive situation in statistics in which the Bayesian and frequentist approaches to a hypothesis testing problem give different results for certain choices of the prior distribution. The problem of the disagreement between the two approaches was discussed in Harold Jeffreys’ 1939 textbook; it became known as Lindley’s paradox after Dennis Lindley called the disagreement a paradox in a 1957 paper. 
JeffriesMatusita Distance  JeffriesMatusita Distance calculates the separability of a pair of probability distributions. This can be particularly meaningful for evaluating the results of Maximum Likelihood classifications. varSel 
Jensen  This paper introduces Jensen, an easily extensible and scalable toolkit for productionlevel machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, LBFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.). This framework makes it possible to deploy and train models with a few lines of code, and also extend and build upon this by integrating new loss functions and optimization algorithms. 
JensenShannon Distance (JSD) 
In probability theory and statistics, the JensenShannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius (IRad) or total divergence to the average. It is based on the KullbackLeibler divergence, with some notable (and useful) differences, including that it is symmetric and it is always a finite value. The square root of the JensenShannon divergence is a metric often referred to as JensenShannon distance. 
jLDADMM  In this technical report, we present jLDADMM—an easytouse Java toolkit for conventional topic models. jLDADMM is released to provide alternatives for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the onetopicperdocument Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. jLDADMM is opensource and available to download at: https://…/jLDADMM 
JMP  SAS created JMP in 1989 to empower scientists and engineers to explore data visually. Since then, JMP has grown from a single product into a family of statistical discovery tools, each one tailored to meet specific needs. All of our software is visual, interactive, comprehensive and extensible. 
Job Safety Analysis (JSA) 
A Job Safety Analysis (JSA) is one of the risk assessment tools used to identify and control workplace hazards. A JSA is a second tier risk assessment with the aim of preventing personal injury to a person, or their colleagues, and any other person passing or working adjacent, above or below. JSAs are also known as Activity Hazard Analysis (AHA), Job Hazard Analysis (JHA) and Task Hazard Analysis (THA). 
Joint and Individual Variation Explained (JIVE) 
Research in several fields now requires the analysis of datasets in which multiple highdimensional types of data are available for a common set of objects. In particular, The Cancer Genome Atlas (TCGA) includes data from several diverse genomic technologies on the same cancerous tumor samples. In this paper we introduce Joint and Individual Variation Explained (JIVE), a general decomposition of variation for the integrated analysis of such datasets. The decomposition consists of three terms: a lowrank approximation capturing joint variation across data types, lowrank approximations for structured variation individual to each data type, and residual noise. JIVE quantifies the amount of joint variation between data types, reduces the dimensionality of the data, and provides new directions for the visual exploration of joint and individual structure. The proposed method represents an extension of Principal Component Analysis and has clear advantages over popular twoblock methods such as Canonical Correlation Analysis and Partial Least Squares. A JIVE analysis of gene expression and miRNA data on Glioblastoma Multiforme tumor samples reveals genemiRNA associations and provides better characterization of tumor types. r.jive 
Joint and Progressive Learning strAtegY (JPlay) 
Despite the fact that nonlinear subspace learning techniques (e.g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (outofsamples), and costeffectiveness (linearization). To this end, a novel linearized subspace learning technique is developed in a joint and progressive way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning str\textbf{a}teg\textbf{y} (JPlay), with its application to multilabel classification. The JPlay learns highlevel and semantically meaningful feature representation from highdimensional data by 1) jointly performing multiple subspace learning and classification to find a latent subspace where samples are expected to be better classified; 2) progressively learning multicoupled projections to linearly approach the optimal mapping bridging the original space with the most discriminative subspace; 3) locally embedding manifold structure in each learnable latent subspace. Extensive experiments are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous stateoftheart methods. 
Joint Approximate Diagonalization of Eigenmatrices (JADE) 

Joint Greedy Equivalence Search (jointGES) 
We consider the problem of jointly estimating multiple related directed acyclic graph (DAG) models based on highdimensional data from each graph. This problem is motivated by the task of learning gene regulatory networks based on gene expression data from different tissues, developmental stages or disease states. We prove that under certain regularity conditions, the proposed $\ell_0$penalized maximum likelihood estimator converges in Frobenius norm to the adjacency matrices consistent with the datagenerating distributions and has the correct sparsity. In particular, we show that this joint estimation procedure leads to a faster convergence rate than estimating each DAG model separately. As a corollary we also obtain highdimensional consistency results for causal inference from a mix of observational and interventional data. For practical purposes, we propose jointGES consisting of Greedy Equivalence Search (GES) to estimate the union of all DAG models followed by variable selection using lasso to obtain the different DAGs, and we analyze its consistency guarantees. The proposed method is illustrated through an analysis of simulated data as well as epithelial ovarian cancer gene expression data. 
Joint Matrix Factorization  Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering the hidden features and partbased patterns from highdimensional data. NMF and its variants have been successfully applied into diverse fields such as pattern recognition, signal processing, data mining, bioinformatics and so on. Recently, NMF has been extended to analyze multiple matrices simultaneously. However, a unified framework is still lacking. In this paper, we introduce a sparse multiple relationship data regularized joint matrix factorization (JMF) framework and two adapted prediction models for pattern recognition and data integration. Next, we present four update algorithms to solve this framework. The merits and demerits of these algorithms are systematically explored. Furthermore, extensive computational experiments using both synthetic data and real data demonstrate the effectiveness of JMF framework and related algorithms on pattern recognition and data mining. 
Joint Maximum Likelihood Estimation (JMLE) 
JMLE ‘Joint Maximum Likelihood Estimation’ is also called UCON, ‘Unconditional maximum likelihood estimation’. It was devised by Wright & Panchapakesan, http://www.rasch.org/memo46.htm. In this formulation, the estimate of the Rasch parameter (for which the observed data are most likely, assuming those data fit the Rasch model) occurs when the observed raw score for the parameter matches the expected raw score. ‘Joint’ means that the estimates for the persons (rows) and items (columns) and rating scale structures (if any) of the data matrix are obtained simultaneously. 
Joint Probability Distribution  In the study of probability, given at least two random variables X, Y, …, that are defined on a probability space, the joint probability distribution for X, Y, … is a probability distribution that gives the probability that each of X, Y, … falls in any particular range or discrete set of values specified for that variable. In the case of only two random variables, this is called a bivariate distribution, but the concept generalizes to any number of random variables, giving a multivariate distribution. 
Joint Random Forest (JRF) 
JRF 
Joint Sequence Fusion (JSFusion) 
We present an approach named JSFusion (Joint Sequence Fusion) that can measure semantic similarity between any pairs of multimodal sequence data (e.g. a video clip and a language sentence). Our multimodal matching network consists of two key components. First, the Joint Semantic Tensor composes a dense pairwise representation of two sequence data into a 3D tensor. Then, the Convolutional Hierarchical Decoder computes their similarity score by discovering hidden hierarchical matches between the two sequence modalities. Both modules leverage hierarchical attention mechanisms that learn to promote wellmatched representation patterns while prune out misaligned ones in a bottomup manner. Although the JSFusion is a universal model to be applicable to any multimodal sequence data, this work focuses on videolanguage tasks including multimodal retrieval and video QA. We evaluate the JSFusion model in three retrieval and VQA tasks in LSMDC, for which our model achieves the best performance reported so far. We also perform multiplechoice and movie retrieval tasks for the MSRVTT dataset, on which our approach outperforms many stateoftheart methods. 
JointDNN  Deep neural networks are among the most influential architectures of deep learning algorithms, being deployed in many mobile intelligent applications. Endside services, such as intelligent personal assistants (IPAs), autonomous cars, and smart home services often employ either simple local models or complex remote models on the cloud. Mobileonly and cloudonly computations are currently the status quo approaches. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side, but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloudonly approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward and backward propagation in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18X and 32X reductions on the latency and mobile energy consumption of querying DNNs, respectively. 
JointGAN  A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain. The proposed framework consists of multiple generators and a single softmaxbased critic, all jointly trained via adversarial learning. From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full joint distribution. Most examples considered are for joint analysis of two domains, with examples for three domains also presented. 
JointPolicy Correlation  To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simplest form is independent reinforcement learning (InRL), where each agent treats its experience as part of its (nonstationary) environment. In this paper, we first observe that policies learned using InRL can overfit to the other agents’ policies during training, failing to sufficiently generalize during execution. We introduce a new metric, jointpolicy correlation, to quantify this effect. We describe an algorithm for general MARL, based on approximate best responses to mixtures of policies generated using deep reinforcement learning, and empirical gametheoretic analysis to compute metastrategies for policy selection. The algorithm generalizes previous ones such as InRL, iterated best response, double oracle, and fictitious play. Then, we present a scalable implementation which reduces the memory requirement using decoupled metasolvers. Finally, we demonstrate the generality of the resulting policies in two partially observable settings: gridworld coordination games and poker. 
Joyplot  joyplot: a series of histograms, density plots or time series for a number of data segments, all aligned to the same horizontal scale and presented with a slight overlap. 
jQuery  jQuery is a fast, small, and featurerich JavaScript library. It makes things like HTML document traversal and manipulation, event handling, animation, and Ajax much simpler with an easytouse API that works across a multitude of browsers. With a combination of versatility and extensibility, jQuery has changed the way that millions of people write JavaScript. 
JSONstat  JSONstat is a simple lightweight JSON dissemination format best suited for data visualization, mobile apps or open data initiatives, that has been designed for all kinds of disseminators. JSONstat also proposes an HTML microdata schema to enrich HTML tables and put the JSONstat vocabulary in the browser. Fortunately, there are already tools that ease the use of JSONstat, like the JSONstat Javascript Toolkit, a library to process JSONstat responses. 
Jubatus  Jubatus is a distributed processing framework and streaming machine learning library. Jubatus includes these functionalities: · Online Machine Learning Library: Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering · Feature Vector Converter (fv_converter): Data Preprocess and Feature Extraction · Framework for Distributed Online Machine Learning with Fault Tolerance 
JUMPER  In early years, text classification is typically accomplished by featurebased machine learning models; recently, deep neural networks, as a powerful learning machine, make it possible to work with raw input as the text stands. However, exiting endtoend neural networks lack explicit interpretation of the prediction. In this paper, we propose a novel framework, JUMPER, inspired by the cognitive process of text reading, that models text classification as a sequential decision process. Basically, JUMPER is a neural system that scans a piece of text sequentially and makes classification decisions at the time it wishes. Both the classification result and when to make the classification are part of the decision process, which is controlled by a policy network and trained with reinforcement learning. Experimental results show that a properly trained JUMPER has the following properties: (1) It can make decisions whenever the evidence is enough, therefore reducing total text reading by 3040% and often finding the key rationale of prediction. (2) It achieves classification accuracy better than or comparable to stateoftheart models in several benchmark and industrial datasets. 
Jumping Knowledge Network (JKN) 
Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of ‘neighboring’ nodes that a node’s representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture — jumping knowledge (JK) networks — that flexibly leverages, for each node, different neighborhood ranges to enable better structureaware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves stateoftheart performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models’ performance. 
Juniper  Nonconvex mixedinteger nonlinear programs (MINLPs) represent a challenging class of optimization problems that often arise in engineering and scientific applications. Because of nonconvexities, these programs are typically solved with global optimization algorithms, which have limited scalability. However, nonlinear branchandbound has recently been shown to be an effective heuristic for quickly finding highquality solutions to largescale nonconvex MINLPs, such as those arising in infrastructure network optimization. This work proposes Juniper, a Juliabased opensource solver for nonlinear branchandbound. Leveraging the highlevel Julia programming language makes it easy to modify Juniper’s algorithm and explore extensions, such as branching heuristics, feasibility pumps, and parallelization. Detailed numerical experiments demonstrate that the initial release of Juniper is comparable with other nonlinear branchandbound solvers, such as Bonmin, Minotaur, and Knitro, illustrating that Juniper provides a strong foundation for further exploration in utilizing nonlinear branchandbound algorithms as heuristics for nonconvex MINLPs. 
Jupyter  The Jupyter Notebook is a web application for interactive data science and scientific computing. It allows users to author documents that combine livecode with narrative text, equations, images, video and visualizations. These documents encode a complete and reproducible record of a computation that can be shared with others on GitHub, Dropbox and the Jupyter Notebook Viewer. 
Just Another Gibbs Sampler (JAGS) 
Just another Gibbs sampler (JAGS) is a program for simulation from Bayesian hierarchical models using Markov chain Monte Carlo (MCMC), developed by Martyn Plummer. JAGS has been employed for statistical work in many fields, for example ecology, management, and genetics. JAGS aims for compatibility with WinBUGS/OpenBUGS through the use of a dialect of the same modeling language (informally, BUGS), but it provides no GUI for model building and MCMC sample postprocessing, which must therefore be treated in a separate program (for example calling JAGS from R through a library such as rjags and postprocessing MCMC output in R). The main advantage of JAGS in comparison to the members of the original BUGS family (WinBUGS and OpenBUGS) is its platform independence. It is written in C++, while the BUGS family is written in Component Pascal, a less widely known programming language. In addition, JAGS is already part of many repositories of Linux distributions such as Ubuntu. It can also be compiled as a 64bit application on 64bit platforms, thus making all the addressable space available to BUGS models. JAGS can be used via the command line or run in batch mode through script files. This means that there is no need to redo the settings with every run and that the program can be called and controlled from within another program (e.g. from R via rjags as outlined above). JAGS is licensed under the GNU General Public License. 
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