[PDF] Top 20 Learning and Inference over Constrained Output
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Learning and Inference over Constrained Output
... second, inference is used to maintain struc- tural consistency only after learning (learning plus inference (L+I)), and finally inference is used while learning the pa- rameters ... See full document
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Learning Natural Language Inference with LSTM
... Recently, Bowman et al. (2015) released the Stan- ford Natural Language Inference (SNLI) corpus for the purpose of encouraging more learning-centered approaches to NLI. This corpus contains around 570K ... See full document
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Random Walk Inference and Learning in A Large Scale Knowledge Base
... and inference in a large scale knowledge base containing imperfect knowledge with incomplete ...soft inference procedure based on a combination of constrained, weighted, random walks through the ... See full document
11
Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
... In modern computer science education, massive open online courses (MOOCs) log thousands of hours of data about how students solve coding challenges. Being so rich in data, these platforms have garnered the interest of ... See full document
9
Learning Auxiliary Fronting with Grammatical Inference
... are more interested in merely identifying the lan- guage (weak learning). In both communities, the best performing algorithms that learn from raw posi- tive data only 1 , generally rely on some combination of ... See full document
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Active inference and learning
... responses over 32 trials: this figure reports the behavioural and (simulated) physiological responses during successive ...format) over the 11 policies ...summed over time (black bars) and reaction ... See full document
19
Inference And Learning: Computational Difficulty And Efficiency
... In this chapter we will study regression and model selection problem focusing on two aspects: (a) model misspecification; (b) model class can be non-convex. For the first point, classic decision theory is concerned with ... See full document
242
Formal and Empirical Grammatical Inference
... A. Kasprzik and T. K¨otzing. 2010. String extension learning using lattices. In Henning Fernau Adrian- Horia Dediu and Carlos Mart´ın-Vide, editors, Pro- ceedings of the 4th International Conference on Lan- guage ... See full document
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Typed Graph Models for Learning Latent Attributes from Names
... of learning a person’s ethnicity from his/her name as an inference prob- lem over typed graphs, where the edges represent la- beled relations between features that are parameter- ized by the edge ... See full document
5
Markov Logic Network: Unify Framework for Ontology Learning
... find inference and weight learning we use MLN method to learning the weight of ...weight learning method to produce the good ...the inference values. For finding the inference we ... See full document
6
Secrecy Constrained Distributed Inference in Wireless Sensor Networks
... statistical inference, all the data is collected and processed in a centralized ...Distributed inference, however, detects signal presence, estimates parameters and tracks targets based on distributed data ... See full document
146
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
... variational inference (VI) (Wainwright and Jordan, 2008), Markov chain Monte Carlo (MCMC) (Gilks et ...variational inference (Hoffman et ...standard learning schemes have also been suc- cessfully ... See full document
37
Unified Expectation Maximization
... Driven Learning (Chang et ...the constrained inference step in UEM we present an efficient dual projected gradient as- cent algorithm which generalizes several dual decomposition and Lagrange ... See full document
11
Privacy Preserving Fuzzy Modeling for Secure Multiparty Computation
... BP learning input and output relation for learning data is represented as weights of neurons of neural network, but it is difficult to know the interpretability of input and output ... See full document
6
Integer Linear Programming in NLP Constrained Conditional Models
... machine learning based natural language processing systems including an award winning semantic parser, and has presented invited talks in several international conferences, and several tutorials on machine ... See full document
6
Discriminative Learning over Constrained Latent Representations
... features over this intermediate representation, thus separat- ing learning into two stages – specifying the la- tent representation, and then extracting features for ...an inference process using ... See full document
9
Models and Inference for Prefix Constrained Machine Translation
... We apply phrase-based and neural models to a core task in interactive machine trans- lation: suggesting how to complete a par- tial translation. For the phrase-based sys- tem, we demonstrate improvements in sug- gestion ... See full document
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A General Purpose Algorithm for Constrained Sequential Inference
... Semantic Parsing and Code Generation. In semantic parsing and code generation, constraints ensure both the syntactic validity and the exe- cutability of the output. For instance, for the predi- cate I S A DJACENT ... See full document
11
An Online Algorithm for Learning over Constrained Latent Representations using Multiple Views
... the output of a structure prediction algorithm ...unsupervised learning of (latent) structure prediction with a supervised learning approach for the ... See full document
5
Gradient-Based Inference for Networks with Output Constraints
... optimize over a model parameters rather than a single dual ...the output configurations with the ...of inference by removing mass from invalid outputs—in much the same way a dual variable affects the ... See full document
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