Generative models of music are in need of performance and gesture additions, i.e. inclusions of subtle temporal and dynamic alterations, and gestures so as to render the music musical. While much of the research regarding music generation is based on music theory, the work presented here is based on the temporal perception, which is divided into three parts, the immediate (subchunk), the short-...

In this paper I discuss some general problems one is confronted with when trying to analyze the utterance of words within concrete conceptual and contextual settings and to go beyond the aspects of meaning typically investigated by a contrastive analysis of lexemes within the Katz-Fodor tradition of semantics. After emphasizing some important consequences of the traditional view, several phenom...

Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear. In this paper, we argue that adversarial learning, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such ...

Generative design, which integrates multidisciplinary types of expertise in unconventional ways, was reserved just until recently to experienced and highly autodidactic designers. However, growing recognition of the importance of generative design methodologies have resulted in a need to introduce theories and applications of generative design to undergraduate students as part of their design s...

to evaluate the yield and yield components in additive intercropping system of grain sorghum (sorghum bicolor l.) and cowpea (vigna unguiculata l.) under limited irrigation condition, an experiment was conducted in research farm of college of agriculture, university of tehran, during spring 2006. the experimental treatments were arranged in split plots based on a complete randomized block desig...

One of the main limitations for the functional scalability of computer automated design systems is the representation used for encoding designs. Using computer programs as an analogy, representations can be thought of as having the properties of combination, control-flow and abstraction. We define generative representations as those which have the ability to reuse elements in an encoding throug...

Score functions induced by generative models extract fixeddimensions feature vectors from different-length data observations by subsuming the process of data generation, projecting them in highly informative spaces called score spaces. In this way, standard discriminative classifiers such as support vector machines, or logistic regressors are proved to achieve higher performances than a solely ...

Modern machine learning techniques often use discriminative models that require large amounts of labeled data. Since generating labeled training data sets is expensive, an alternative approach is to use a generative model, which leverages a simple heuristic to weakly label data. Domain experts prefer using generative models because they “tell a story” about their data. Unfortunately, generative...