Search results for "generative model"
showing 7 items of 17 documents
The intentional stance as structure learning: a computational perspective on mindreading
2015
Recent theories of mindreading explain the recognition of action, intention, and belief of other agents in terms of generative architectures that model the causal relations between observables (e.g., observed movements) and their hidden causes (e.g., action goals and beliefs). Two kinds of probabilistic generative schemes have been proposed in cognitive science and robotics that link to a "theory theory" and "simulation theory" of mindreading, respectively. The former compares perceived actions to optimal plans derived from rationality principles and conceptual theories of others' minds. The latter reuses one's own internal (inverse and forward) models for action execution to perform a look…
Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
2020
This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the sugg…
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA
2023
Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…
Supporting fine-grained generative model-driven evolution
2010
Published version of an article in the journal: Software and Systems Modeling. Also available on SpringerLink:http://dx.doi.org/10.1007/s10270-009-0144-1 In the standard generative Model-driven Architecture (MDA), adapting the models of an existing system requires re-generation and restarting of that system. This is due to a strong separation between the modeling environment and the runtime environment. Certain current approaches remove this separation, allowing a system to be changed smoothly when the model changes. These approaches are, however, based on interpretation of modeling information rather than on generation, as in MDA. This paper describes an architecture that supports fine-gra…
The role of synergies within generative models of action execution and recognition: A computational perspective
2015
Controlling the body – given its huge number of degrees of freedom – poses severe computational challenges. Mounting evidence suggests that the brain alleviates this problem by exploiting “synergies”, or patterns of muscle activities (and/or movement dynamics and kinematics) that can be combined to control action, rather than controlling individual muscles of joints [1–10]. D’Ausilio et al. [11] explain how this view of motor organization based on synergies can profoundly change the way we interpret studies of action recognition in humans and monkeys, and in particular the controversy on the “granularity” of the mirror neuron system (MNs): whether it encodes either (lower) kinematic aspects…
Organizational Learning, Innovation and Internationalization: A Complex System Model
2013
Research on organizational learning, innovation and internationalization has traditionally linked these concepts through linear causality, by considering any one of them as the cause of another, an approach that might be considered contradictory and static. This paper aims to clarify these relationships and proposes a dynamic theoretical model that has mutual causality at its core and is based on ideas originating in complexity theory. The final model results from case studies of two clothing sector firms. The authors consider that the three concepts constitute a complex system and can adapt and transcend, as any alteration can take the system to the edge of chaos. Adaptability is fostered …
Researching Conditional Probability Problem Solving
2014
The chapter is organized into two parts. In the first one, the main protagonist is the conditional probability problem. We show a theoretical study about conditional probability problems, identifying a particular family of problems we call ternary problems of conditional probability. We define the notions of Level, Category and Type of a problem in order to classify them into sub-families and in order to study them better. We also offer a tool we call trinomial graph that functions as a generative model for this family of problems. We show the syntax of the model that allows researchers and teachers to translate a problem in terms of the trinomial graphs language, and the consequences of th…