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Topic: Generative model

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	Joint versus Conditional Densities - Pros and Cons
		Generative models and joint densities can be computed using reliable techniques for maximum likelihood and maximum a posteriori estimation.
		Unlike conditional densities (discriminative models), a joint density or generative model is optimized over the whole dimensionality and thus models all the relationships between the variables in a more equal manner.
		Thus, as long as the discriminative model is not too computationally intensive and the volume of data is tractable, training on all the data is not a problem.
	vismod.media.mit.edu /tech-reports/TR-507/node33.html (946 words)

	``Snakes - Active Contour Models'' Citations
		Model fitting is achieved by optimizing an objective function consisting of two components, one that measures the degree of grey level correspondence between the model and the data, and the other that measures how well the boundary of the model fits the data.
		In general, the solutions proposed in the past solved this problem only partially: either the mathematical model encoding the figure-ground problem was too simple or the optimization methods that were used were not efficient enough or they could not guarantee to find the global minimum of the cost function describing the figure-ground model.
		For deformable models and templates, the extraction of a shape is obtained through the minimization of an energy composed of an internal regularization term (not necessary in the case of parametric models) and an external attraction potential.
	iacl.ece.jhu.edu /projects/gvf/gvf_cite/snake_cite_abstract.html (16470 words)

	Phil/Psych 446, Week 8
		In place of a supervisor, specify a generative model of the way in which the environment is assumed to generate data.
		To model high level cognition, a neural network must be able to distinguish between a dog chasing a cat and a cat chasing a dog, and also be able to represent the higher level relation between scratching and chasing.
		Hummel and Holyoak (1997) proposed a synchrony model (LISA) of analogical mapping and retrieval.
	cogsci.uwaterloo.ca /courses/Phil446/Phil446.week8.html (1772 words)

	Generative Grammar and a Speech Production Model (Site not responding. Last check: 2007-10-14)
		Modelling in linguistics falls into this category: it is a set of established descriptions along with a number of hypotheses about language.
		Whatever the criticisms, TGG has been a fruitful model, characterizing knowledge that was needed for language processing: it has given rise to sets of hypotheses that have provided insights into the possible nature of language, and suggested hypotheses for researchers in other fields as to the nature of mind, and of processing within the brain.
		The model shows the distinction between competence and performance at the phonetic level: knowledge bases as the competence model of this part of the system, and the decision algorithm as performance.
	www.essex.ac.uk /speech/archive/k-th/k-th-1.html (3520 words)

	Preface to the Proceedings of GMBV 2002
		As an example, the dynamical model underlying the Kalman filter (defining the temporal evolution of the state variables) would be descriptive, while the observation model (defining the relationship between state variables and observations) would be generative.
		By this definition, the dynamical model of the Kalman filter is not descriptive, and neither, by itself, generative; but it is a component of a generative model.
		Often, the generative model is of use not only to the research scientist in the formulation of an algorithm, but also to the algorithm itself as a component of an iterative estimation process: this use of direct models was strongly emphasized by MacKay [2] and Kawato et al.
	www.diku.dk /publikationer/tekniske.rapporter/2002/02-01/preface.html (1555 words)

	AAAI Proceedings Template
		It generalizes existing generative models of music performance expression and extends them to deliver representations of musical timing and dynamics that can be composed into overall expression profiles.
		Although these generative models explain musical expression based on one kind of musical structure (e.g., meter, phrase, surface), an overall theory is lacking that describes how these different components are combined in performance.
		Note that in the literature no generative models, nor systematic studies, are found on the relationship between rhythmic or figural structure, and expressive timing, even though this source seem to account for a large proportion of the variance in the expressive signal [Drake and Palmer, 1993].
	www.nici.kun.nl /mmm/papers/dh-97-c/dh-97-c.html (1702 words)

	Pustejovsky's Generative Lexicon Model (Site not responding. Last check: 2007-10-14)
		Pustejovsky's lexicon model attempts to explain semantic problems such as the polymorphic nature of language and the creative use of words in novel contexts.
		The idea of a generative lexical model is contrasted to a more usual sense enumerative lexicon, where each word has a literal meaning and lexical ambiguity is treated by multiple listing of words.
		This could be applied to the generative lexicon, for example in a phrase like "Steven King began a new novel", with the inference "Steven King began to write a new novel" given by the information contained in the noun phrase "Steven King" and the "agentive" value of novel which is (informally) "someone writes a novel".
	www-users.cs.york.ac.uk /~mdeboni/research/generative_lexicon.html (1308 words)

	Graphical Models
		An alternative technique, popular in the UAI community, is to start with an initial guess of the model structure (i.e., at a specific point in the lattice), and then perform local search, i.e., evaluate the score of neighboring points in the lattice, and move to the best such point, until we reach a local optimum.
		In a model with hidden variables, it might be less than this.) The first term is just the likelihood and the second term is a penalty for model complexity.
		Classical control theory is mostly concerned with the special case where the graphical model is a Linear Dynamical System and the utility function is negative quadratic loss, e.g., consider a missile tracking an airplane: its goal is to minimize the squared distance between itself and the target.
	www.cs.ubc.ca /~murphyk/Bayes/bayes.html (6598 words)

	CANADA BP-II.20 - Best Practices on Indigenous Knowledge (Site not responding. Last check: 2007-10-14)
		This ‘best practice’ is called the ‘Generative Curriculum Model.’ The curriculum and its outcomes are not pre-determined, but rather are ‘generated’ each time the programme is delivered, in order to reflect the unique indigenous knowledge and the particular needs, goals, and circumstances of the communities participating in the programme.
		Although the Generative Curriculum Model was not conceived within the crucible of scholarly post-modernist discourse, the First Nations partners and we share a ‘post-modernist’ valuing of multiple voices and an insistence upon situating alternative constructions of experiences with reference to the historical, cultural, political, and personal contexts in which these constructions were generated.
		The Generative Curriculum Model is an approach to building on indigenous knowledge to create capacity in a variety of settings around the world across a range of subjects, especially in areas of social/human service and education.
	www.unesco.org /most/bpik20-2.htm (5472 words)

	Generative Oscillation - A Cognitive Model for Language Production - Thor May (Site not responding. Last check: 2007-10-14)
		The GO model takes a much broader view of repetition than is normally found in linguistics, considering a cline from local (often idiosyncratic) g-ripples to the global g-ripples such as lexical items which have become formal, generalized tokens in the language.
		Repetition is generally unconscious, and may relate to the topic, to rhyme or prosidy, to grammatical felicity, or simply to being accessible in memory.
		The GO model opts unequivocally for an emergent view of language in which the inner and outer human environments grow together, are interdependent, obey the same natural laws, and are ultimately part of the same macro ecology.
	thormay.net /lxesl/go1.html (15927 words)

	Creating Generative Models from Range Images
		By using general algebraic models-the generative models proposed by Snyder-we are able to subsume many of the previous approaches in computer vision and recover high-level models from incomplete range data.
		The input to our algorithm beside the range data is an input model class or hierarchy including an algebraic model description from which the model can be evaluated, constraints on curves in the final model, and code for an initial guess for the root model of the hierarchy.
		The left images are of the banana and bowl recovered using the spoon hierarchy while the rotating generalized cylinder is used in the last two to recover the ladle and spoon.
	graphics.stanford.edu /papers/invgen (742 words)

	Supplementary Videos
		This paper presents an adaptive discriminative generative model that generalizes the conventional Fisher Linear Discriminant algorithm and renders a proper probabilistic interpretation.
		We model the appearance of the tracked object using probabilistic principle component analysis, and propose a method to update this generative model online.
		That is, without the proposed discriminative generative model, negative samples may be mistakenly selected to update our subspace model.
	www.ifp.uiuc.edu /~rlin1/adgm.html (544 words)

	PSI - Computational Molecular Biology
		To properly account for uncertainties when detecting gene structures, we have taken a Bayesian approach and developed GenRate, a generative model that accounts for both genome-wide expression data and co-location and density of probes in DNA sequence data.
		The model has a number of local minima that is exponential in the length of the DNA sequence data, so direct application of the EM learning algorithm produces poor results.
		The model explains the observed values, consisting of measured transcription for exon body and junction probes, as a weighted linear combination of the abundance of the alternative isoforms with scale dependent noise and an outlier process.
	www.psi.utoronto.ca /cmb.html (1879 words)

	[No title] (Site not responding. Last check: 2007-10-14)
		Example: Robbie has a simulator for his world.
		The same as a generative model, except that you control the random numbers.
		Example: Robbie has direct control over the random numbers in his simulator.
	hunch.net /~jl/projects/comparison/generative_model.html (33 words)

	Template (Site not responding. Last check: 2007-10-14)
		The generative learning model is a teaching sequence based on the view that knowledge is constructed by the learner.
		Osborne and Freyberg, advocates of the generative learning model have identified three distinct phases to the the model, in addition to the preliminary phase, namely: focus, challenge, and application.
		Note that the major difference is the identification of a preliminary stage in the generative model, otherwise the phases correlate.
	scied.gsu.edu /Hassard/mos/7.6.html (978 words)

	A Generative Model for CALL Development
		The term "generative" is intended to be taken in the linguistic, but not syntactic, sense.
		In linguistics we refer to a generative grammar, which is a set of rules which generates new utterances.
		The Model does not contain any new material; rather, it is a synthesis of elements from Instructional Design, existing commentaries on CALL development, foreign language standards, lesson plan design, and educational psychology.
	www.geocities.com /CollegePark/Library/8960 (542 words)

	Concurrency Abstracts (Site not responding. Last check: 2007-10-14)
		These models are investigated within the context of PCCS, an extension of Milner's SCCS in which each summand of a process summation expression is guarded by a probability and the sum of these probabilities is 1.
		We also show that the models form a hierarchy: the reactive model is derivable from the generative model by abstraction from the relative probabilities of different actions, and the generative model is derivable from the stratified model by abstraction from the purely probabilistic branching structure.
		The general benefits of the approach are that it is conceptually straightforward, involves fewer artificial constructs than many competing models of concurrency, yet is applicable to a considerably wider range of types of systems, including systems with buses and ethernets, analog systems, and real-time systems.
	boole.stanford.edu /abstracts.html (9620 words)

	Design Document
		Generative Learning Theory, pioneered by Merlin Wittrock places learners in a very active role, requiring them to generate their own meaning as they interact with the content.
		According to Wittrock, understanding and comprehension are generated through the process of creating relationships between prior concepts and the new content, as conveyed by or embodied in message stimuli.
		This interactive will generate time point and time series graphs of solar insolation (input to the top or the Earth's atmosphere) as functions of the Earth's orbital variations (obliquity, eccentricity, precession).
	www.comet.ucar.edu /~dowens/paleo/design.htm (2001 words)

	Caltech Computer Science Technical Reports - Creating Generative Models from Range Images
		Using simple acquisition techniques and a user-defined class of models, our method produces a simple and intuitive object description that is relatively insensitive to noise and is easy to manipulate and edit.
		Our technique for model recovery and subsequent manipulation and editing is demonstrated on real objects -- a spoon, bowl, ladle, and cup -- using a simple tree of possible generative models.
		We believe that higher-level model representations are extremely important, and their recovery for actual objects is a fertile area of research towards which this thesis is a step.
	caltechcstr.library.caltech.edu /187 (382 words)

	A Generative Model for Music Transcription (ResearchIndex) (Site not responding. Last check: 2007-10-14)
		Our model, formulated as a Dynamical Bayesian Network, embodies a transparent and computationally tractable approach to this acoustic analysis problem.
		An advantage of our approach is that it places emphasis on explicitly modelling the sound generation procedure.
		Generative Model Based Polyphonic Music Transcription - Ali Taylan Cemgil (2003)
	citeseer.ist.psu.edu /674814.html (626 words)

	[No title]
		In the present work, a neural network technique which can recognise complex relationships was employed to develop a quantitative method for estimating the yeild and tensile strengths as a function of steel composition and rolling parameters.
		The model was tested extensively to confirm that the predictions are reasonable in the context of metallurgical principles and other data published in the literature.
		The latter result is employed to derive a generalization of the construction of Phelps, which is shown to give rise to some perfect codes that are nonequivalent to the perfect codes obtained from the known constructions.
	www.inference.phy.cam.ac.uk /mackay/docs/bibs.bib (4288 words)

	A SEGMENT-BASED GENERATIVE MODEL OF SPEECH
		We have presented a simple segmental Hidden Markov Model for analyzing speech waveforms directly in the time domain and derived an efficient algorithm for MAP inference in this model.
		We highlight that the appeal of our model is that it enables a wide range of applications in a single framework.
		We filled in the corrupted region by generating new segments with periods between the two bounding voiced regions.
	www.psi.utoronto.ca /~kannan/segmental (482 words)

	LIINC \| Publications (Site not responding. Last check: 2007-10-14)
		Paul Sajda and Kyungim Baek, (2004) Integration of form and motion within a generative model of visual cortex.
		Wielaard and P. Sajda (2003) Simulated Optical Imaging of Orientation Preference in a Model of V1, Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering Capri Island, Italy, March 20-22, 2003, 499-502.
		Wielaard and P Sajda (2004) Revisiting Hubel and Wiesel: Classification of simple and complex cells in a spiking neuron model of macaque striate cortex.
	liinc.bme.columbia.edu /liinc_pubs.htm (2215 words)

	Stanford NLP Group
		Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency.
		A Generative Constituent-Context Model for Improved Grammar Induction.
		Parsing with Treebank Grammars: Empirical Bounds, Theoretical Models, and the Structure of the Penn Treebank.
	www-nlp.stanford.edu /publications.shtml (1940 words)

	A Generative Model for Filtering Thresholds (ResearchIndex) (Site not responding. Last check: 2007-10-14)
		Abstract: This paper presents a generative model of score distribution, focused on the case of information filtering, where sampling of training data is not random.
		Parameters of the model were estimated using the Maximum Likelihood Principle, conjugate priors, and conjugate gradient descent.
		Using Language Models for Tracking Events of Interest Over..
	citeseer.ist.psu.edu /454558.html (421 words)

	Home page for GMBV 2004
		estimation and/or maximization of the posterior probability (given an image or image sequence) of model parameters (state variables).
		Often, the generative model is used not only by the software developer in the formulation of the algorithm, but also by the algorithm itself as a component of an iterative estimation process.
		papers which focus on a detailed study of generative models (e.g.
	www.diku.dk /users/aecp/GMBV (441 words)

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