[PDF] Top 20 On Smoothing and Inference for Topic Models
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On Smoothing and Inference for Topic Models
... of inference algorithm? In this paper, we provide convincing empirical evidence that points in a different direction, namely that the claimed differences can be explained away by the different settings of two ... See full document
8
Scalable Collapsed Inference for High Dimensional Topic Models
... up topic models (Anandku- mar et ...Bayesian inference, which leads to lower data efficiency, and sometimes unreliable ...scalable inference algorithm for topic mod- ...posed ... See full document
10
Spatial smoothing in Bayesian models: a comparison of weights matrix specifications and their impact on inference
... Defining the spatial weights matrix based on adjacency or distances between geographic neighbours seems to provide a good model fit regardless of the spatial autocor- relation inherent in the risk surface. In particular, ... See full document
16
Fast Automatic Smoothing for Generalized Additive Models
... but inference based on the resulting fit is ...of smoothing as part of the regression ...automatic smoothing is via basis function expansion using reduced rank smoothing; this is the ... See full document
27
Variational Inference in Nonconjugate Models
... Many models of interest, however, do not enjoy the properties required to take advantage of this easily derived ...nonconjugate models 1 include Bayesian logistic regression (Jaakkola and Jordan, 1997), ... See full document
27
A Latent Concept Topic Model for Robust Topic Inference Using Word Embeddings
... effective topic inference in conventional topic ...erbate topic inference, since word co-occurrence statistics becomes more sparse as the number of documents ... See full document
7
Online Multilingual Topic Models with Multi Level Hyperpriors
... For topic models, such as LDA, that use a bag-of-words assumption, it becomes es- pecially important to break the corpus into appropriately-sized ...the models are estimated solely from the term ... See full document
6
Authorship Attribution with Topic Models
... of topic-based author representations that go beyond traditional authorship ...three topic models (LDA, AT, and DADT) for several scenarios where the number of authors varies from three to about ... See full document
42
Unsupervised Topic Modelling for Multi Party Spoken Discourse
... probabilistic topic models (Hofmann, 1999; Blei et ...each topic is a probability dis- tribution over ...its topic is the same as that of the previous ...discuss inference in this ... See full document
8
Nonparametric Spherical Topic Modeling with Word Embeddings
... existing topic models are appropriate to leverage such correla- ...base topic model and propose an efficient algorithm based on Stochastic Variational ...of topic coherence on two different ... See full document
6
Distributed Algorithms for Topic Models
... widely-used topic models, namely the Latent Dirichlet Allocation (LDA) model, and the Hierarchical Dirichet Process (HDP) ...and inference is done in a parallel, distributed ...learning topic ... See full document
28
Adaptive Smoothing Method Based on Fuzzy Theory Study and Realization
... Information integration is the utilization of multiple sources of information to extract higher quality than any single integrated information process information [3]. At present, the field has produced some uncertainty ... See full document
6
Bayesian Hidden Topic Markov Models
... with topic modeling; topic models offer a statistical model of textual ...Markov models is proposed using a fully Bayesian ...for topic modeling, its underlying assumptions ignore the ... See full document
120
Polylingual Topic Models
... Bilingual topic models for parallel texts with word-to-word alignments have been studied pre- viously using the HM-bitam model (Zhao and Xing, ...bilingual topic models. Both of these ... See full document
10
Why ADAGRAD Fails for Online Topic Modeling
... Probabilistic topic models (Blei, 2012) are pop- ular algorithms for uncovering hidden thematic structure in ...Probabilistic topic modeling usually requires computing a posterior distribution over ... See full document
6
Variational methods for geometric statistical inference
... The k-means method is an iterative clustering algorithm which associates each ob- servation with one of k clusters. It traditionally employs cluster centers in the same space as the observed data. By relaxing this ... See full document
150
GS-OPT: A new fast stochastic algorithm for solving the non-convex optimization problem
... In this paper, we propose GS-OPT, a new algorithm solving efficiently the non-convex optimization problems. Using Bernoulli distribution and stochastic approximations, we provide the GSOPT algorithm to deal well with the ... See full document
10
Stochastic Variational Inference
... consider topic models. Topic models are prob- abilistic models of text used to uncover the hidden thematic structure in a collection of documents (Blei, ...a topic model is that ... See full document
45
Low dimensional Embeddings for Interpretable Anchor based Topic Inference
... tively finding anchor words by eliminating words that are reproducible by other words (Arora et al., 2012) is impractical. The anchor words se- lected by the greedy algorithm are overly eccen- tric, particularly at the ... See full document
10
Lexicosyntactic Inference in Neural Models
... This work is inspired by recent work in recasting various semantic annotations into natural language inference (NLI) datasets (White et al., 2017; Po- liak et al., 2018a,b; Wang et al., 2018) to gain a better ... See full document
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