Factorization Machine (FM) provides a generic framework that combines the prediction quality of factorization models with the flexibility of feature engineering that discriminative models like SVM offer. The Bayesian Factorization Machine [11], with its impressive predictive performance and the convenience of automatic tuning of parameters, has been one of the most successful and efficient approaches within this framework. However, this model has two major drawbacks. Firstly, it assumes that the data is generated from Gaussian distributions that may not be the best assumption for count data such as integer-valued ratings. Secondly, to get the best performance, one needs to cross-validate over the number of latent factors used for modeling the pairwise interaction in FM, a process that is computationally intensive. This paper introduces the Nonparametric Poisson Factorization Machine (NPFM), which models count data using the Poisson distribution, which provides both modeling and computational advantages for sparse data. The ideal number of latent factors is estimated from the data itself, thereby addressing a key limitation of existing approaches to FM. Additionally, NPFM has linear time complexity with respect to the number of non-zero observations.

Article Causality based generation of directed test cases Share on Authors: Nina Saxena Comp. Eng. Res. Ctr., University Of Texas At Austin, Austin, TX Comp. Eng. Res. Ctr., University Of Texas At Austin, Austin, TXView Profile , Jacob Abraham Comp. Eng. Res. Ctr., University Of Texas At Austin, Austin, TX Comp. Eng. Res. Ctr., University Of Texas At Austin, Austin, TXView Profile , Avijit Saha IBM, Austin, 11400 Burnet Rd., Austin, TX IBM, Austin, 11400 Burnet Rd., Austin, TXView Profile Authors Info & Claims ASP-DAC '00: Proceedings of the 2000 Asia and South Pacific Design Automation ConferenceJanuary 2000 Pages 503–508https://doi.org/10.1145/368434.368771Online:28 January 2000Publication History 3citation183DownloadsMetricsTotal Citations3Total Downloads183Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In the past, hardware design validation has relied primarily on simulation. New techniques such as model checking have been introduced but no objective study investigating the advantages such techniques provide over simulation has been made. Simulation is model checking over a trace elicited by executing a test vector; model checking can be viewed as exhaustive simulation. Each has its own set of advantages and limitations. A platform, "Sherlock", was available wherein one could use properties or specifications expressed as CTL-like formulae interchangeably for checking simulation runs or for model checking. In this paper we describe and present results from an experimental study undertaken on a real implementation to better understand the efficacies of the two methods. We also present improved methods for accommodating liveness, fairness (of arbitration) and existence conditions in simulation and outline some techniques for writing implementation-independent properties for model checking.

"Verification" of large multiprocessor designs currently heavily on simulation. Formal techniques such as model checking are typically only applied to small parts of the system, due to issues of computational and notational complexity. With these two facts in mind the authors have designed a platform which aims to help bridge the gap between formal verification and simulation. They present a temporal logic specification language which includes constructs for specifying system behavior at a high level of abstraction, and discuss its use in simulation and model checking.

This paper concerns neural network approaches to function approximation and optimization using linear superposition of Gaussians (or what are popularly known as radial basis function (RBF) networks). The problem of function approximation is one of estimating an underlying function f, given samples of the form ((y/sub i/, x/sub i/); i=1,2,...,n; with y/sub i/=f(x/sub i/)). When the dimension of the input is high and the number of samples small, estimation of the function becomes difficult due to the sparsity of samples in local regions. The authors find that this problem of high dimensionality can be overcome to some extent by using linear transformations of the input in the Gaussian kernels. Such transformations induce intrinsic dimension reduction, and can be exploited for identifying key factors of the input and for the phase space reconstruction of dynamical systems, without explicitly computing the dimension and delay. They present a generalization that uses multiple linear projections onto scalars and successive RBF networks (MLPRBF) that estimate the function based on these scaler values. They derive some key properties of RBF networks that provide suitable grounds for implementing efficient search strategies for nonconvex optimization within the same framework.>