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An Autoencoder Approach to Learning Bilingual Word Representations
Cross-language learning allows one to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment ofExpand
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EPOpt: Learning Robust Neural Network Policies Using Model Ensembles
Sample complexity and safety are major challenges when learning policies with reinforcement learning for real-world tasks, especially when the policies are represented using rich functionExpand
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Correlational Neural Networks
Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigmsExpand
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Diversity driven attention model for query-based abstractive summarization
Abstractive summarization aims to generate a shorter version of the document covering all the salient points in a compact and coherent fashion. On the other hand, query-based summarization highlightsExpand
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An algebraic approach to abstraction in reinforcement learning
To operate effectively in complex environments learning agents ignore irrelevant details. Stated in general terms this is a very difficult problem. Much of the work in this field is specialized toExpand
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Latent dirichlet allocation based multi-document summarization
Extraction based Multi-Document Summarization Algorithms consist of choosing sentences from the documents using some weighting mechanism and combining them into a summary. In this article we useExpand
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Accurate mobile robot localization in indoor environments using bluetooth
In this paper, we describe an accurate method for localization of a mobile robot using bluetooth. We introduce novel approaches for obtaining distance estimates and trilateration that overcome theExpand
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Efficient Computation of the Shapley Value for Centrality in Networks
The Shapley Value is arguably the most important normative solution concept in coalitional games. One of its applications is in the domain of networks, where the Shapley Value is used to measure theExpand
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Efficient Computation of the Shapley Value for Game-Theoretic Network Centrality
The Shapley value--probably the most important normative payoff division scheme in coalitional games--has recently been advocated as a useful measure of centrality in networks. However, although thisExpand
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Model Minimization in Hierarchical Reinforcement Learning
When applied to real world problems Markov Decision Processes (MDPs) often exhibit considerable implicit redundancy, especially when there are symmetries in the problem. In this article we present anExpand
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