A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Preserved Fulltext
In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification. ...
The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened ...
., 2016] proposed a Bayesian nonparametric approach to learn the number of label-feature correlation patterns automatically. ...
doi:10.24963/ijcai.2018/434
dblp:conf/ijcai/ZhangLNZXL18
fatcat:m2tjnhfqs5czhod3shiuq73hnu
Nonparametric Online Machine Learning with Kernels
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and ...
It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications. ...
A notable approach to relax the grid search is to use multiple kernel learning (MKL) [Gönen and Alpaydın, 2011] . ...
doi:10.24963/ijcai.2017/758
dblp:conf/ijcai/Nguyen17
fatcat:yeu276gfxbgyraxsptqggdvkya
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a Bayesian nonparametric topic model to automatically infer this number, based on the hierarchical Dirichlet process and the notion of latent Dirichlet allocation. ...
Extensive experiments on various text data sets show that: (a) compared with parametric approaches that use pre-specified true number of new categories, the proposed nonparametric approach can yield comparable ...
Algorithm 1 Collapsed Gibbs Sampling for LBPL-NTM Input: the words W, the number of topics L, parameter ζ of the base Dirichlet distribution H, the hyper-parameters β, aγ, bγ, aα, bα, and the maximal number ...
arXiv:1910.04420v1
fatcat:3gmtt5x4wreh5hi2e4qvokhul4
A unified view of generative models for networks: models, methods, opportunities, and challenges
[article]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Differences between models generally stem from different philosophical choices about how to learn from data or different empirically-motivated goals. ...
For instance, novel theoretical models and optimization techniques developed in machine learning are largely unknown within the social and biological sciences, which have instead emphasized model interpretability ...
For example, how can we understand what is being traded off when we use a latent block model under a Bayesian nonparametric learning paradigm versus a general latent space model under a frequentist learning ...
arXiv:1411.4070v1
fatcat:6z2nwwdxlnhb5cxflpprgosmza
Predicting Market Impact Costs Using Nonparametric Machine Learning Models
2016
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we employed the state-of-the-art nonparametric machine learning models: neural networks, Bayesian neural network, Gaussian process, and support vector regression, to predict market impact ...
As a result, most nonparametric machine learning models outperformed a-state-of-the-art benchmark parametric model such as I-star model in four error measures. ...
Acknowledgments The data set used in this work was collected by using the Bloomberg Terminal, which can be accessed by subscribing to the Bloomberg Professional service. ...
doi:10.1371/journal.pone.0150243
pmid:26926235
pmcid:PMC4771170
fatcat:jptiayliuvgvfhvb4wkpmnmdgm
Nonparametric Bayesian Modeling for Automated Database Schema Matching
[article]
2015
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Our experiments show that our method is more accurate and faster than the existing instance-based matching algorithms in part because of the use of nonparametric Bayesian models. ...
We introduce a schema matching framework that builds nonparametric Bayesian models for each field and compares them by computing the probability that a single model could have generated both fields. ...
Finally, we show by experiments that the success of our approach is attributable (at least in part) to the properties of nonparametric Bayesian models.
Sensitivity to Data Size. ...
arXiv:1507.01443v1
fatcat:zbx35dh24nhkjapptjvm6qfpve
The Indian Chefs Process
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To the best of our knowledge, the ICP is the first Bayesian nonparametric model supporting every possible DAG. ...
This paper introduces the Indian Chefs Process (ICP), a Bayesian nonparametric prior on the joint space of infinite directed acyclic graphs (DAGs) and orders that generalizes Indian Buffet Processes. ...
To our knowledge, no Bayesian nonparametric structure learning methods have been applied to deep learning models. ...
arXiv:2001.10657v1
fatcat:dg4qrbeckfeklh5ile2a647bnq
An Infinite Mixture of Inverted Dirichlet Distributions
[chapter]
2011
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The proposed mixture is learned using a fully Bayesian approach and allows to overcome a challenging issue when dealing with data clustering namely the automatic selection of the number of clusters. ...
The results show that the proposed approach is effective for positive data modeling when compared to those reported using infinite Gaussian mixture. ...
The goal of this paper is to extend our work in [7] to the infinite case by considering a nonparametric Bayesian approach namely Dirichlet processes which have a history going back to Antoniak [8] . ...
doi:10.1007/978-3-642-24958-7_9
fatcat:2koqx5jfmzd3tppeu6iw6gfyt4
Categorization as nonparametric Bayesian density estimation
[chapter]
2008
The Probabilistic Mind:
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Categorization as nonparametric Bayesian density estimation 2 Categorization as nonparametric Bayesian density estimation Rational models of cognition aim to explain the structure of human thought and ...
In this chapter, we pursue a rational analysis of category learning: inferring the structure of categories from a set of stimuli labeled as belonging to those categories. ...
From a Bayesian perspective, the nonparametric approach requires us to use priors that include as broad a range of densities of possible, thereby allowing us to infer very complex densities if they are ...
doi:10.1093/acprof:oso/9780199216093.003.0014
fatcat:g5y7fmeehngtbniymjkqcpp5h4
Bayesian Nonparametric View to Spawning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov ...
Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death. ...
Due to the efficiency of Bayesian nonparametric modeling, in [1] - [4] , authors employed nonparametric Bayesian modeling to improve the tracking of multiple objects. ...
arXiv:2112.06640v1
fatcat:des52vsbezggpc6wlxrqbka67u
Probabilistic Machine Learning: Models, Algorithms and a Programming Library
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms ...
, a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds. ...
We built a nonparametric Bayesian model to identify worker reliability and task clarity without the assumption of ground truth labels [Tian and Zhu, 2012] . ...
doi:10.24963/ijcai.2018/823
dblp:conf/ijcai/Zhu18
fatcat:6cnqt2gmg5abre73cac7w2ya6y
Nonparametric Variational Auto-encoders for Hierarchical Representation Learning
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation ...
The resulting model induces a hierarchical structure of latent semantic concepts underlying the data corpus, and infers accurate representations of data instances. ...
FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. ...
arXiv:1703.07027v2
fatcat:fllrzupobrfhjpjeejjgzgmve4
Bayesian Nonparametric Learning of How Skill Is Distributed across the Mutual Fund Industry
2019
Federal Reserve Bank of Atlanta, Working Papers
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Applying our Bayesian nonparametric learning approach to a panel of actively managed, domestic equity funds, we find the population distribution of skill to be fat-tailed, skewed towards higher levels ...
In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. ...
In Section 6 we apply our Bayesian nonparametric learning approach along with a Bayesian parametric hierarchical model and a idiosyncratic Bayesian parametric model to a panel of 5,136 actively managed ...
doi:10.29338/wp2019-03
fatcat:pmmqyxdbg5cqdgrdnylprjqybq
Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering ...
This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction ...
We first revisit the basic concept of primitives and then introduce the related Bayesian nonparametric methods to learn them.
A. ...
arXiv:2003.00759v2
fatcat:wygdmojgvndixbrez2hblmijpq
The Future of Data Analysis in the Neurosciences
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis ...
We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences. ...
Instead of fitting one parameter to each variable to predict a behavior or clinical outcome with linear regression, GPs learn a nonparametric distribution of non-linear functions to explain brain-behavior ...
arXiv:1608.03465v1
fatcat:roen4d2axncufftj3ifjjimqpe
« Previous
Showing results 1 — 15 out of 9,284 results