Dual Decomposition Learning with Latent Variables
Learning Density Models via Structured Latent Variables
... ducted independently and separately), we propose to learn both the optimal subspace and the parameters for GMM jointly. Specifically, we engage the Mixtures of Factor Analy- sers (MFA) [99] where a common factor loading ...
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Doc2hash: Learning Discrete Latent variables for Documents Retrieval
... In this paper, we propose a generative model with a categorical distribution prior for semantic hashing which learns binary codes of documents in an end-to-end manner. To train the genera- tive model, instead of using ...
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Learning Linear Cyclic Causal Models with Latent Variables
... with latent variables), both LLC and DCG find quite good estimates of the causal structure, when sufficient samples are ...of latent confounding is surprisingly good given that the DCG model does not ...
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Ridge Regression Learning Algorithm in Dual Variables
... a dual version of the Ridge Regression ...ANOVA decomposition method from the kernel corresponding to splines with an infi- nite number of ...the dual version of Ridge Regression is applied to the ...
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Learning loopy graphical models with latent variables: Efficient methods and guarantees
... discrete latent models on loopy graphs in high dimensions (which can also be easily be extended to Gaussian models; see remarks following Theo- rem 2 ...consider learning latent Gaussian graph- ical ...
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Learning loopy graphical models with latent variables: Efficient methods and guarantees
... local latent tree structure based on information distances as shown in Figure 2(f), since they are approximately ...local latent trees are discovered, and distances between nearby hid- den nodes are ...the ...
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The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
... We emphasize that the results present in this section are alternatives that did not exist before in previous approaches for learning mixed graph structures through variational methods (e.g., Silva and Scheines, ...
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Fast and Robust Compressive Summarization with Dual Decomposition and Multi Task Learning
... a dual decomposition frame- work for multi-document summarization, using a model that jointly extracts and compresses ...multi-task learning frame- work to take advantage of existing data for ...
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Spectral Dependency Parsing with Latent Variables
... to latent vari- able dependency trees like us but under the restric- tive conditions that model parameters are trained for a specified, albeit arbitrary, tree ...
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Dynamic asset allocation and latent variables
... his learning in this setting is limited since µ t is ...more learning is possible in the sense that the investor does not increase the precision on his estimate on µ t over ...
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Object and Action Classification with Latent Variables
... WITH LATENT VARIABLES (a) (b) (c) (d) (e) Figure 6: Representative crop-split examples from the Graz-02 dataset the learning algorithm with easy examples and to then gradually feed in more complex ...
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Improved Learning Of Structural Support Vector Machines: Training With Latent Variables And Nonlinear Kernels
... CHAPTER 4 STRUCTURAL SVMS FOR PROTEIN SEQUENCE ALIGNMENTS In the last two chapters we introduced structural support vector machines and the cutting plane algorithm for solving the associated optimization prob- lem during ...
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Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood
... the variables are indexed in an ordered fashion, there are usually reasonable choices for the underlying graph structure; for example, graphs based on nearest-neighbors are often used for specifying time series ...
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Factorization of Latent Variables in Distributional Semantic Models
... Value Decomposition The high dimensionality of the co-occurrence ma- trix makes it necessary in most practical applica- tions to apply some form of dimensionality re- duction to F , with the goal of finding a ba- ...
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Regularizing Flat Latent Variables with Hierarchical Structures
... Figure 9: Inner topic number sensibility of 2-level STM with 100 leaf topics on 20 Newsgroups directly modeling and inferring hierarchical structured top- ics, we applied a hierarchical clustering method to capture the ...
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An integral approach to causal inference with latent variables
... handle latent variables with- out explicitly modeling them ...structure learning from observational and experimental data, parameter learning, probabilistic inference, and, quantitative causal ...
Dual Decomposition with Many Overlapping Components
... many variables and subproblems are left untouched after the first few rounds. Finally, Fig. 4 compares the runtimes of our im- plementation of DD-ADMM with those achieved by a state-of-the-art LP solver, CPLEX, in ...
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Combining handcrafted features with latent variables in machine learning for prediction of radiationâ induced lung damage
... representation learning in case ...representation learning with the aid of classification task improved the prediction over the conventional separate training with latent sizes 1 – 8 as shown in ...
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Piecewise Latent Variables for Neural Variational Text Processing
... the learning of power- ful directed graphical models with con- tinuous latent variables, such as varia- tional ...multi-modal latent factors in real-world data, such as natural language ...the ...
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