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Multi-Conditional Learning: Generative/Discriminative Training for
Multi-Conditional Learning: Generative/Discriminative Training for

... marginal conditionals distributions of some observed variables given others that are of particular interest so as to form our multi-conditional objective. Importantly, using a globally normalized joint distribution with this construction it is also possible to derive two consistent conditional model ...
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d - Fizyka UMK

Bounded Rationality www.AssignmentPoint.com Bounded rationality
Bounded Rationality www.AssignmentPoint.com Bounded rationality

... their rationality is limited by the information they have, the cognitive limitations of their minds, and the time available to make the decision. Decision-makers in this view act as satisficers who can only seek a satisfactory solution, lacking the ability and resources to arrive at the optimal one. ...
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Human Vision: Electrophysiology and Psychophysics

... the brain was composed of a continuous system of wires or whether it was a discontinuous network made up of individual neurons ...
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Logical and Probabilistic Knowledge Representation and Reasoning

... [33] Parag Singla and Pedro Domingos. Entity Resolution with Markov Logic. Proc. ICDM, 2006, pp. 572-582. [34] Guy Van den Broeck. Lifted Inference and Learning in Statistical Relational Models. PhD Thesis, KU Leuven, 2013. [35] Guy Van den Broeck, Wannes Meert and Adnan Darwiche. Skolemization for ...
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Computer Projects Assignment

... Machine links not only the original sequences, but creates links between their subsequences by using a sequence reduction procedure that eliminate related subsequences from the original sequence. ...
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... • If neuron A repeatedly and persistently contributes to the firing of neuron B, than the connection between A and B will get stronger. • If neuron A does not contribute to the firing of neuron B for a long period of time, than the connection between A and B becomes weaker. ...
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Neural modeling fields

Neural modeling field (NMF) is a mathematical framework for machine learning which combines ideas from neural networks, fuzzy logic, and model based recognition. It has also been referred to as modeling fields, modeling fields theory (MFT), Maximum likelihood artificial neural networks (MLANS).This framework has been developed by Leonid Perlovsky at the AFRL. NMF is interpreted as a mathematical description of mind’s mechanisms, including concepts, emotions, instincts, imagination, thinking, and understanding. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
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