A unified view of graph regularity via matrix decompositions

Plasticity of the neural coding metaphor: An unnoticed rhetoric in scientific discourse

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900133x ◽

2019 ◽

Vol 42 ◽

Author(s):

Giulia Frezza ◽

Pierluigi Zoccolotti

Keyword(s):

Discourse Analysis ◽

Neural Coding ◽

Scientific Discourse ◽

Convincing Argument ◽

Unified View ◽

Brain Behavior

Abstract The convincing argument that Brette makes for the neural coding metaphor as imposing one view of brain behavior can be further explained through discourse analysis. Instead of a unified view, we argue, the coding metaphor's plasticity, versatility, and robustness throughout time explain its success and conventionalization to the point that its rhetoric became overlooked.

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Toward a unified view of media annotation

Ingénierie des systèmes d information ◽

10.3166/isi.7.5-6.45-63 ◽

2002 ◽

Vol 7 (5-6) ◽

pp. 45-53

Author(s):

Claude Christment ◽

Florence Sèdes

Keyword(s):

Unified View ◽

Media Annotation

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From Stove-pipe to Network Centric Leveraging Technology to Present a Unified View

10.21236/ada466489 ◽

2004 ◽

Author(s):

Medhat A. Abuhantash ◽

Matthew V. Shoultz

Keyword(s):

Unified View

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A Unified View of Models Describing the Avoidance of Superparasitism

Mathematical Medicine and Biology A Journal of the IMA ◽

10.1093/imammb/6.3.161 ◽

1989 ◽

Vol 6 (3) ◽

pp. 161-178 ◽

Cited By ~ 5

Author(s):

D. J. DALEY ◽

J. H. MAINDONALD

Keyword(s):

Unified View

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A Unified View of Causal and Non-causal Feature Selection

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3436891 ◽

2021 ◽

Vol 15 (4) ◽

pp. 1-46

Author(s):

Kui Yu ◽

Lin Liu ◽

Jiuyong Li

Keyword(s):

Feature Selection ◽

Bayesian Network ◽

Synthetic Data ◽

Selection Methods ◽

Bayesian Network Model ◽

Real World Data ◽

Feature Sets ◽

Unified View ◽

Optimal Feature ◽

Different Levels

In this article, we aim to develop a unified view of causal and non-causal feature selection methods. The unified view will fill in the gap in the research of the relation between the two types of methods. Based on the Bayesian network framework and information theory, we first show that causal and non-causal feature selection methods share the same objective. That is to find the Markov blanket of a class attribute, the theoretically optimal feature set for classification. We then examine the assumptions made by causal and non-causal feature selection methods when searching for the optimal feature set, and unify the assumptions by mapping them to the restrictions on the structure of the Bayesian network model of the studied problem. We further analyze in detail how the structural assumptions lead to the different levels of approximations employed by the methods in their search, which then result in the approximations in the feature sets found by the methods with respect to the optimal feature set. With the unified view, we can interpret the output of non-causal methods from a causal perspective and derive the error bounds of both types of methods. Finally, we present practical understanding of the relation between causal and non-causal methods using extensive experiments with synthetic data and various types of real-world data.

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Modelling ourselves: what the free energy principle reveals about our implicit notions of representation

Synthese ◽

10.1007/s11229-021-03140-5 ◽

2021 ◽

Author(s):

Matt Sims ◽

Giovanni Pezzulo

Keyword(s):

Free Energy ◽

Philosophy Of Mind ◽

Predictive Processing ◽

Energy Principle ◽

Philosophical Debate ◽

Free Energy Principle ◽

Self Organized ◽

Functional Aspects ◽

Structural Content ◽

Unified View

AbstractPredictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a normative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., representational versus non-representational. Here we argue that these different interpretations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to distinguish four main notions of representation, which focus on organizational, structural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that traditionally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demonstrates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what representations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate.

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