[PDF] Top 20 Latent Variable Model for Multi modal Translation

... of multi-task learning and estimate a joint distribution over trans- lations and ...to model the interaction between visual and textual features through a latent ...This latent vari- able can ... See full document

... discriminative multi-scale grammars and to compare them to generatively es- timated latent variable ...tive model split into initial NNPs (San, Wall) and final NNPs (Francisco, ... See full document

... traditional multi-view facial expression recognition method is adopted view-specific classifier to recognition view-specific ...a multi-view facial expression recognition method based on Discriminative ... See full document

... a model that is consistent with several types of established data, data unexplained by the WHO ...biological model is consistent with the observation that multiple diseases share common risk factors ...The ... See full document

... topic model is statistical model which can obtain the abstract patterns of topics in a collection of ...the latent semantic structure of text body. Latent Dirichlet Allocation: is an ... See full document

... methods in a range of low-rank estimation problems involving simulated as well as real data. The tasks involving real data are on estimating user-preference ma- trices for recommender systems and identifying signatures ... See full document

... a multi-source attention-based NMT model trained to translate a pair of sentences in two different source languages into a target lan- guage, and reported considerable improvements over a single-source ...a ... See full document

... a multi-level generative model that reasons jointly about latent topics and geographical ...our model recovers coherent top- ics and their regional variants, while identi- fying geographic ... See full document

... machine translation (MNMT) models by incorporat- ing global features extracted from the im- age into attention based ...synthetic multi-modal, multilingual data generated us- ing phrase-based ... See full document

... a model with additional latent variables to increase its expressiveness is known as hidden or latent variable learning (Smith, 2011) and is a promising research direction with success- ful ... See full document

... a model using attention based NMT, where regional and global visual fea- tures are attached in parallel with multiple encod- ing threads and each thread is followed by the text ...baseline model BLEU score ... See full document

... our model is that low dimensionality of word embeddings plays a key theoretical role —unlike in previous papers where the model is agnostic about the di- mension of the embeddings, and the superiority of ... See full document

... history-based model to predict the most likely derivation of a synchronous dependency parser for both syntactic and semantic ...probabilistic model is based on Incremental Sig- moid Belief Networks (ISBNs), ... See full document

... Finally, we sampled 117 paraphrasing pairs and evaluated them based on a paraphrasing per- spective: whether a paraphrase is contextually independent. There were 71 contextually inde- pendent paraphrases and 37 ... See full document

... including multi- pitch estimation (which is considered to be the core problem of transcription), onset/offset detection, instrument identification, and rhythmic ...on multi-pitch detection and automatic ... See full document

... the model. Some techniques consider a more expres- sive model by using features defined over mention- cluster or cluster-cluster (Rahman and Ng, 2011c; Stoyanov and Eisner, 2012; Haghighi and Klein, ...tic ... See full document

... machine translation (SMT) has seen a resurgence in popularity in recent years, with progress being driven by a move to phrase-based and syntax-inspired ...the translation process. Discrimi- native models of ... See full document

... PCA, model selection is non-trivial because a higher fit percentage can always be achieved by selecting more com- ...the model, we selected a model with, respectively 3, 2 and 2 components for the ... See full document

... learning latent bases from time varying, non-negative data 0 . We model the temporal dependence in data by introducing a dynamic Dirichlet prior ...proposed model, and derive a maximum a posteriori ... See full document

... The latent-variable model is inte- grated into the standard MT pipeline by comput- ing marginal probabilities for each rule in the parse forest of a source sentence using the algorithm in Figure 1 ... See full document