Clique Inference Process for Solving Max-CSP

2006 ◽  
pp. 746-750 ◽  
Author(s):  
Mohand Ou Idir Khemmoudj ◽  
Hachemi Bennaceur
Keyword(s):  
Inference Process

scholarly journals How Computational Modeling Can Force Theory Building in Psychological Science

2021 ◽  
pp. 174569162097058
Author(s):  
Olivia Guest ◽  
Andrea E. Martin
Keyword(s):  
Computational Modeling ◽  
Computational Model ◽  
Process Modeling ◽  
Theory Building ◽  
Formal Modeling ◽  
Inference Process ◽  
Experimental Practice ◽  
Psychological Science ◽  
Scientific Inference ◽  
And Behaviors

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

Toward a predictive model of the consumer inference process: The role of expertise

1994 ◽  
Vol 11 (2) ◽  
pp. 109-127 ◽  
Author(s):  
Dong Hwan Lee ◽  
Richard W. Olshavsky
Keyword(s):  
Predictive Model ◽  
Inference Process

Asymmetry of the Trait Inference Process in Impression Formation

1969 ◽  
Vol 28 (3) ◽  
pp. 715-720 ◽  
Author(s):  
Clyde Hendrick
Keyword(s):  
Impression Formation ◽  
Inference Process ◽  
The Asymmetry ◽  
The Relationship ◽  
Very High

Symmetry of the trait inference process was studied by having Ss rate how much trait X implied trait Y and how much trait Y implied trait X for each of 36 pairs of traits. Desirability of the traits ranged from very high to very low. The relationship between traits within a pair was varied from consistent to highly inconsistent. Differences between the implication ratings for the two orders of presentation revealed that a definite asymmetry in the strength of the inference existed. The direction of the asymmetry varied as a function of the degree of inconsistency between traits within pairs. The results were discussed in terms of a differential discounting process.

NENOK — A SOFTWARE ARCHITECTURE FOR GENERIC INFERENCE

2010 ◽  
Vol 19 (01) ◽  
pp. 65-99 ◽  
Author(s):  
MARC POULY
Keyword(s):  
Programming Languages ◽  
Software Library ◽  
Inference Process ◽  
Software Projects ◽  
Open Problems ◽  
Constraint Systems ◽  
Fast Prototyping ◽  
Inference Algorithms ◽  
Key Competences ◽  
High Level

Computing inference from a given knowledgebase is one of the key competences of computer science. Therefore, numerous formalisms and specialized inference routines have been introduced and implemented for this task. Typical examples are Bayesian networks, constraint systems or different kinds of logic. It is known today that these formalisms can be unified under a common algebraic roof called valuation algebra. Based on this system, generic inference algorithms for the processing of arbitrary valuation algebras can be defined. Researchers benefit from this high level of abstraction to address open problems independently of the underlying formalism. It is therefore all the more astonishing that this theory did not find its way into concrete software projects. Indeed, all modern programming languages for example provide generic sorting procedures, but generic inference algorithms are still mythical creatures. NENOK breaks a new ground and offers an extensive library of generic inference tools based on the valuation algebra framework. All methods are implemented as distributed algorithms that process local and remote knowledgebases in a transparent manner. Besides its main purpose as software library, NENOK also provides a sophisticated graphical user interface to inspect the inference process and the involved graphical structures. This can be used for educational purposes but also as a fast prototyping architecture for inference formalisms.

scholarly journals Attention as a Bayesian inference process

2011 ◽  
Author(s):  
Sharat Chikkerur ◽  
Thomas Serre ◽  
Cheston Tan ◽  
Tomaso Poggio
Keyword(s):  
Bayesian Inference ◽  
Inference Process

scholarly journals Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 7839-7846
Author(s):  
Junliang Guo ◽  
Xu Tan ◽  
Linli Xu ◽  
Tao Qin ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Fine Tuning ◽  
Inference Process ◽  
Neural Machine Translation ◽  
Training Strategy ◽  
Speed Up ◽  
Good Improvement ◽  
Tuning Process ◽  
Translation Accuracy ◽  
The Cost

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines.

scholarly journals The self as a prior for the other: social learning under paranoia

2021 ◽  
Author(s):  
Joseph M Barnby ◽  
Nichola Raihani ◽  
Peter Dayan
Keyword(s):  
Bayesian Inference ◽  
Social Interactions ◽  
Social Preferences ◽  
Value Orientation ◽  
Social Value ◽  
The Self ◽  
The Other ◽  
Social Value Orientation ◽  
Inference Process ◽  
Social Partners

To benefit from social interactions, people need to predict how their social partners will behave. Such predictions arise through integrating prior expectations with evidence from observations, but where the priors come from and whether they influence the integration is not clear. Furthermore, this process can be affected by factors such as paranoia, in which the tendency to form biased impressions of others is common. Using a modified social value orientation (SVO) task in a large online sample (n=697), we showed that participants used a Bayesian inference process to learn about partners, with priors that were based on their own preferences. Paranoia was associated with preferences for earning more than a partner and less flexible beliefs regarding a partner’s social preferences. Alignment between the preferences of participants and their partners was associated with better predictions and with reduced attributions of harmful intent to partners.

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