11 papers · ranked by Valyu relevance
Richard L. Warr, David B. Dahl, Jeremy M. Meyer, Arthur Lui
We propose the attraction Indian buffet distribution (AIBD), a distribution for binary feature matrices influenced by pairwise similarity information. Binary feature matrices are used in Bayesian models to uncover latent variables (i.e., features) that explain observed data. The Indian buffet process (IBP) is a popular…
Shonosuke Sugasawa, Daichi Mochihashi
We develop a new stochastic process called spatially-dependent Indian buffet processes (SIBP) for binary feature matrices of unbounded columns with spatial correlations between subjects, and propose general spatial factor models for various multivariate response variables. We introduce spatial dependency through the…
Lancelot F. James, Juho Lee, Abhinav Pandey
Bayesian nonparametric hierarchical priors are highly effective in providing flexible models for latent data structures exhibiting sharing of information between and across groups. Most prominent is the Hierarchical Dirichlet Process (HDP), and its subsequent variants, which model latent clustering between and across…
Lancelot F. James
> Abstract: The purpose of this work is to describe a unified, and indeed simple, mechanism for non-parametric Bayesian analysis, construction and generative sampling of a large class of latent feature models which one can describe as generalized notions of Indian Buffet Processes (IBP). This is done via the Poisson…
Changmin Yu, Maneesh Sahani, Máté Lengyel
Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and…
Melanie F. Pradier, Viktor Stojkoski, Zoran Utkovski, Ljupčo Kocarev + 1 more
'Fernando Pérez‐Cruz'] This paper presents a Bayesian nonparametric latent feature model specially suitable for exploratory analysis of high-dimensional count data. We perform a non-negative doubly sparse matrix factorization that has two main advantages: not only we are able to better approximate the row input…
Pavel Kulmon, Andrzej Stateczny
This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical…
Michael Zhang, Avinava Dubey, Sinead A. Williamson
Indian Buffet Process based models are an elegant way for discovering underlying features within a data set, but inference in such models can be slow. Inferring underlying features using Markov chain Monte Carlo either relies on an uncollapsed representation, which leads to poor mixing, or on a collapsed…
Mei-Yi Wu, Jia-Hong Lee, Chuan-Ying Hsueh, Anastasios Doulamis + 1 more
'Zahir M. Hussain'] In recent years, the technology of artificial intelligence (AI) and robots is rapidly spreading to countries around the world. More and more scholars and industry experts have proposed AI deep learning models and methods to solve human life problems and improve work efficiency. Modern people’s lives…
Chi-Mei Emily Wu, Chih-Ching Teng, Diana De Santis
Food waste has become a significant issue in the foodservice industry. However, food waste management in buffet restaurants has rarely been investigated. Considering the popularity of buffet restaurants in Taiwan, this study serves as the first attempt to identify a corporate management approach to food waste reduction…
Somashekara Hosaagrahara Ramakrishna, Neil Shah, Bhaswati C. Acharyya, Emmany Durairaj + 5 more
'Emmany Durairaj' 'Lalit Verma' 'Srinivas Sankaranarayanan' 'Nishant Wadhwa' 'Carina Venter' 'Mari Maeda-Yamamoto'] Background: Cow’s milk allergy (CMA) is one of the most common and complex food allergies affecting children worldwide and, with a few exceptions, presents in the first few months of life.…