Analyzing human feature learning as nonparametric Bayesian inference.

We draw on recent work in nonparametric Bayesian statistics to define a rational model of human feature learning that forms a featural representation from raw sensory data without pre-specifying the number ... By comparing how the human perceptual system and our rational model use distributional and category information to infer feature representations, we seek to identify some of the forces that govern the ... We approach the problem of feature learning as one of inferring hidden structure from observed data -a problem that can be solved by applying Bayesian inference. ...

dblp:conf/nips/AusterweilG08 fatcat:2uy5px2tczh3beevxvviczcqbq

As guest editors of this special issue on Bayesian nonparametrics (BNP), we are very happy to introduce 19 papers advancing the state-of-the-art in BNP theory and practice. ... The resulting papers combine nonparametric modeling advances with practical learning algorithms to analyze diverse datasets including text documents, social and biological networks, financial and genetic ... Latent feature models provide a framework for learning sparse dictionary-based decompositions of high-dimensional data, as illustrated in "A Bayesian Nonparametric Approach to Image Super-resolution" by ...

doi:10.1109/tpami.2014.2380478 pmid:26598765 fatcat:cuulndonrff4ffvneirnnzariy

Zhou et al., Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection, https://doi. ... Additionally, the detail procedure for anomaly sample definition, the supervised learning dataset collection as well as the data augmentation of insufficient samples are also declared. ... Nonparametric Bayesian Method for Robot Anomaly Diagnose ...

doi:10.1007/978-981-15-6263-1_5 fatcat:gfvoox3mifhc3dutbcxastriu4

Open Access

Bayesian Program Learning (BPL) is an important area of machine learning. ... Secondly, a brief overview of Bayesian model, reasoning algorithm, based on this, a detailed review of Bayesian learning based on speech, assembly, motion learning, bias diagnosis, learning effectiveness ... From then on, the Dirichlet process was used as a priori probability in nonparametric Bayesian. Reasoning Algorithm Bayesian formula as shown in equation 1. ...

doi:10.12783/dtcse/cnai2018/24187 fatcat:tpaeoq5b2rbrfawf5sjf5sezom

The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components ... The efficiency and merits of the proposed nonparametric Bayesian learning approach, while comparing it to other different methods, are demonstrated via two challenging applications, namely texture classification ... The proposed mixture is learned via a fully unsupervised Bayesian nonparametric approach by analyzing data without a priori information on the number of clusters. ...

doi:10.3390/app11135798 fatcat:6jclwgyu2zhkbavyu4xfxpm63q

DOAJ

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. ... Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised learning tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting ...

doi:10.24963/ijcai.2018/823 dblp:conf/ijcai/Zhu18 fatcat:6cnqt2gmg5abre73cac7w2ya6y

The simple rewards are interpreted intuitively as subgoals, which can be used to predict actions or analyze which states are important to the demonstrator. ... The proposed method uses a Bayesian nonparametric mixture model to automatically partition the data and find a set of simple reward functions corresponding to each partition. ... Also, Bayesian nonparametric IRL could be applied to higher-level planning problems where the list of subgoals found by the algorithm may be useful in more richly analyzing the human demonstrator. ...

doi:10.1007/978-3-642-33486-3_10 fatcat:ufhlzmwhwnclzhorif6aohjxnq

We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. ... Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. ... In our nonparametric Bayesian model, F and K need not be known a priori ; these are inferred from the data. ...

arXiv:1206.6486v1 fatcat:va2tqlbefbgyldcufobmpvqiva

As per the literature, healthcare of patients can be analyzed through machine learning tools, and henceforth, in the article, a Bayesian kernel method for medical decision-making problems has been discussed ... Machine learning techniques are useful to deal with large datasets, with an aim to produce meaningful information from the raw information for the purpose of decision making. ... In machine learning problems, kernel is introduced as a similarity measure that can be found as a dot product in feature space. ...

doi:10.4018/ijbdah.20210101.oa3 fatcat:7kaujhqf2rdzjm5updunekaekm

This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application. ... In this tutorial, we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. ... When the parts that compose objects strongly covary across objects, humans treat whole objects as features, whereas individual parts are treated as features if the covariance is weak. ...

doi:10.1016/j.jmp.2011.08.004 fatcat:allxc5i5qbcnvazdss67z7qw3y

Much of human learning and inference can be framed within the computational problem of relational generalization. ... Finally, we combine the analogy- and theory-based learners in a single nonparametric Bayesian model, and show that optimal relational generalization transitions from relying on analogies to building a ... Indeed, much of the knowledge and inferential ability we regard as quintessentially human-learning and acting with little experience in unfamiliar environments, formal reasoning and discovery in mathematics ...

arXiv:2006.04156v1 fatcat:tsmn4bjbprgzznxhqxc2oo2bh4

In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous ... By assuming HDP-HLM as a generative model of observed time series data, and by inferring latent variables of the model, the method can analyze latent double articulation structure, i.e., hierarchically ... and words directly from unsegmented speech signals In this paper, we propose an unsupervised learning method called the nonparametric Bayesian double articulation analyzer (NPB-DAA) which can automatically ...

doi:10.1109/tcds.2016.2550591 fatcat:5nrniqlr5bbz5pc47uelf4izcm

Multiple Versions

This tutorial is a high-level introduction to Bayesian nonparametric methods and contains several examples of their application. ... In this tutorial we describe Bayesian nonparametric methods, a class of methods that side-steps this issue by allowing the data to determine the complexity of the model. ... Rather than needing to be specified in advance, it is determined as part of analyzing the data. In this tutorial, we survey Bayesian nonparametric methods. ...

arXiv:1106.2697v2 fatcat:2s3yprihfzc4rnnhgo3yfpfxxq

Multiple Versions

In particular, we employ the Infinite Hidden Markov Model and Bayesian Nonparametric Factor Analysis for stream data segmentation and pattern discovery. ... Therefore, in this paper we revisit the abnormality detection problem through the lens of Bayesian nonparametric (BNP) and develop a novel usage of BNP methods for this problem. ... Once the features are extracted, latent factors are learned as detailed in Sect. 4.3. ...

doi:10.1007/s40745-015-0030-3 fatcat:56wmpggdgraktipdvg5solnnp4

INDEX TERMS Clustering, mixture models, inverted Dirichlet, nonparametric Bayesian model, stochastic variational inference. ... In this paper, we propose nonparametric hierarchical Bayesian models based on two inverted Dirichlet-based distributions and Pitman-Yor process for positive data features clustering. ... developed in this work to learn the proposed nonparametric hierarchical Bayesian models. ...

doi:10.1109/access.2019.2924651 fatcat:6at4tmwtlzeuxfrbhfher7uyui

DOAJ

Analyzing human feature learning as nonparametric Bayesian inference

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Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics

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Nonparametric Bayesian Method for Robot Anomaly Diagnose [chapter]

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Research Progress in Bayesian Program Learning

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Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

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Probabilistic Machine Learning: Models, Algorithms and a Programming Library

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Bayesian Nonparametric Inverse Reinforcement Learning [chapter]

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Flexible Modeling of Latent Task Structures in Multitask Learning [article]

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Bayesian Kernel Methods

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A tutorial on Bayesian nonparametric models

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Analogy as Nonparametric Bayesian Inference over Relational Systems [article]

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Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition From Continuous Speech Signals

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A Tutorial on Bayesian Nonparametric Models [article]

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Bayesian Nonparametric Approaches to Abnormality Detection in Video Surveillance

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Nonparametric Hierarchical Bayesian Models for Positive Data Clustering Based on Inverted Dirichlet-Based Distributions

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