Mixture Density NetworkHierarchical many-to-many mappingsThe relationship among three correlated variables could be very sophisticated, as a result, we may not be able to find their hidden causality and model their relationship explicitly. However, we......
The whole framework with a mixture of two differentiable density functions is naturally end-to-end trainable. In the experiments, HMDN produces interpretable and diverse candidate samples, and significantly outperforms the state-of-the-art methods on two benchmarks with occlusions, and performs ...
3 for the case of within task specialization. We extend our mixture-of-experts learning experiments from Hihn et al. [36] for supervised and reinforcement learning in Sects. 4.1 and 4.3 and devise a novel application to density estimation in Sect. 4.2. The extended experiments in the ...
Over recent years there has been a rapid advance in our ability to collect data on the movement behaviour of many animal species [4,5] and this has led to the development of sophisticated statistical methods to analyse these data [6]. Statistical methods usually model animal movement as some ...
is assumed thatd < n. Ford ≫ n, dimensionality reduction techniques can be first applied for supervised or unsupervised learning tasks [42,43,44,45,46,47]). Let an unknown parameter vector beθconsisting of meanμas well as covarianceΣ. This will specify the mixture density as...
once again, not a direct target of this work. Instead, we only classified the detected neurons based on their shape and size (see definition above) into two groups, corresponding potentially to granule and pyramidal neurons. The classification was performed using a two-class Gaussian mixture model...
Rationally designing and precisely constructing the dimensions, configurations and compositions of organic nanomaterials are key issues in material chemistry. Nevertheless, the precise synthesis of organic heterostructure nanomaterials remains challengin
Finally, we illustrate our method by a real data example, in which the original and transformed data are fit by the marginal density with different hyperparameters. Keywords: conjugate prior; gamma and inverse gamma distribution; hierarchical model and mixture distribution; marginal density; posterior...
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selection criterion this approach has been used for Gaussian mixture models [11] 1 . Hence, given a parent LTM, the number and position of its children is based on the modelling properties of the children themselves – without any ad-hoc criteria which would be exterior to the model. Previou...