Generative grammar

Résumé

Generative grammar, or generativism ˈdʒɛnərətɪvɪzəm, is a linguistic theory that regards linguistics as the study of a hypothesised innate grammatical structure. It is a biological or biologistic modification of earlier structuralist theories of linguistics, deriving ultimately from glossematics. Generative grammar considers grammar as a system of rules that generates exactly those combinations of words that form grammatical sentences in a given language. It is a system of explicit rules that may apply repeatedly to generate an indefinite number of sentences which can be as long as one wants them to be. The difference from structural and functional models is that the object is base-generated within the verb phrase in generative grammar. This purportedly cognitive structure is thought of as being a part of a universal grammar, a syntactic structure which is caused by a genetic mutation in humans. Generativists have created numerous theories to make the NP VP (NP) analysis work in na

Source officielle

https://en.wikipedia.org/wiki/Generative_grammar

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Bayesian networks as generative models for face recognition

Guillaume Heusch

In this thesis, we address the problem of face modelling by using dedicated statistical generative models, with an application to the face authentication task. Face authentication consists in either accepting or rejecting a user's claim supported by its face image. Classical generative models, such as Gaussian Mixture Models (GMM), Hidden Markov Models (HMM) and their variants have been proved to be successful to tackle this problem. However, these models are not appropriate to the structure of the observed data. In particular, these models implicitly assume independence between features extracted from the image, which is obviously not true in the case of a face. We thus propose new generative models, based on Bayesian Networks, and especially tailored to deal with the object we have to handle: the face. Actually, we would like to exploit as much as a priori knowledge as possible. For this purpose, Bayesian Networks provide an intuitive framework: they allow to encode causal relationships between different kind of random variables, thus enabling to express correlations between different source of information. As a first step, we thus propose a model acting on local observations extracted around salient facial features, which is designed to capture relationships among these pieces of information. The proposed model is shown to be competitive with state-of-the-art approaches based on generative models when applied to the authentication task. At the same level of performance, it is also less complex and thus less time consuming than previous approaches. Besides, and as opposed to classical models, meaningful information could be retrieved from the proposed model. We then extend this model using other sources of information as complementary clues to local features extracted from grayscale face images. Indeed, cognitive studies in face recognition showed that human beings are using various information such as shape, low-resolution representation of the face and also skin color to recognize an individual. We thus proposed new models taking these information into account, and apply them to our authentication problem.

EPFL2010

Chargement

Musical grammar describes a set of principles that are used to understand and interpret the structure of a piece according to a musical style. The main topic of this study is grammar induction for harmony --- the process of learning structural principles from the observation of chord sequences. The question how grammars are learnable by induction from sequential data is an instance of the more general question how abstract knowledge is inducible from the observation of data --- a central question of cognitive science. Under the assumption that human learning approximately follows the principles of rational reasoning, Bayesian models of cognition can be used to simulate learning processes. This study investigates what prior knowledge makes it possible to learn musical grammar inductively from Jazz chord sequences using Bayesian models and computational simulations. The theoretical part of the thesis presents how questions about learnability can be studied in a unified framework involving music analysis, cognitive modeling, Bayesian statistics, and computational simulations. A new grammar formalism, called Probabilistic Abstract Context-Free Grammar (PACFG), is proposed that allows for flexible probability models which facilitate the grammar-induction experiments of this study. PACFG can jointly model multiple musical dimensions such as harmony and rhythm, and can use coordinate ascent variational inference for grammar learning. The empirical part of the thesis reports supervised and unsupervised grammar-learning experiments. To train and evaluate grammar models, a ground-truth dataset of hierarchical analyses of complete Jazz standards, called the Jazz Harmony Treebank (JHT), was created. The supervised grammar-learning experiments, in which grammars for Jazz harmony are learned from the JHT analyses, show that jointly modeling harmony and rhythm significantly improves the grammar models' prediction of the ground truth. The performance and robustness of the grammars are further improved by a transpositionally invariant parameterization of rule probabilities. Following the supervised grammar learning, unsupervised grammar learning was performed by inducing harmony grammars merely from Jazz chord sequences, without the observation of the JHT trees. The results show that the best induced grammar performs similarly well as the best supervised grammar. In particular, the goal-directedness of functional harmony does not need to be assumed a priori, but can be learned without usage of music-specific prior knowledge. The findings of this thesis show that general prior knowledge enables an ideal learner to acquire abstract musical principles by statistical learning. In conclusion, it is plausible that much aspects of musical grammar have been learned by Jazz musicians and listeners, instead of being innate predispositions or explicitly taught concepts. This thesis is moreover embedded into the context of empirical music research and digital humanities. Current studies either describe complex musical structures qualitatively or investigate simpler aspects quantitatively. The computational models developed in this thesis demonstrate that deep insights into music and statistical analyses are not mutually exclusive. They enable a new kind of data-driven music theory and musicology, for instance through comparative analyses of musical grammar for different styles such as Jazz, Rock, and Western classical music.

EPFL2020

Chargement

Face Authentication with Salient Local Features and Static Bayesian Network

Guillaume Heusch, Sébastien Marcel

In this paper, the problem of face authentication using salient facial features together with statistical generative models is adressed. Actually, classical generative models, and Gaussian Mixture Models in particular make strong assumptions on the way observations derived from face images are generated. Indeed, systems proposed so far consider that local observations are independent, which is obviously not the case in a face. Hence, we propose a new generative model based on Bayesian Networks using only salient facial features. We compare it to Gaussian Mixture Models using the same set of observations. Conducted experiments on the BANCA database show that our model is suitable for the face authentication task, since it outperforms not only Gaussian Mixture Models, but also classical appearance-based methods, such as Eigenfaces and Fisherfaces.

2007

Chargement

EPFL2020

Bayesian networks as generative models for face recognition

Guillaume Heusch

EPFL2010