The above description summarizes related work in the hierarchical topic sub topic and threads of themes or sub-topics representation property.
The above description summarizes related work in the aspect based topic representation property.
The above description summarizes related work in the dynamics of story or topic representation.
As mentioned earlier, latent topic representation obtained with Deep Boltzmann Machine-based architecture possesses good generalization ability.
Additionally, undirected models generate distributed latent topic representation and are proven to be superior to the representations obtained with directed topic models for the task of image retrieval.
This, in turn, enhances the modeling power of the network and leads to sparse, parts-based latent topic representation of images.
Using image data from large-scale databases, Boulemden and Tilli [4] reported improved performance of PAM-based latent topic representation in image retrieval operation.
(f(mathbb {w}_{ij})) is the quadratic barrier function which enforces nonnegativity restriction on the model weights, (f Big (p({mathbf{h}} ^{(1)} mid mathbb {U}_{s}) Big )) is the (ell _1 -regularization term which is used to enforce sparsity on the latent topic representation learned by CRSM.
However, images with identical latent topic representations are assumed to contain same semantic concepts and are treated as semantically similar while measuring image similarity.
Li and Perona [33] proposed two variants of LDA that generate intermediate topic representations for natural scene categories, reporting good categorization performance on a large set of complex scenes.
With more time, more data, and additional recurrent neural networks to train models for word and topic representations, Alex and Naveen expect to be able to generate longer funding round summaries that take into account more complex analysis like market competitors.
