Representation Learning
representation learning
... • Two case studies based on autoencoders and convolutional neural networks have been discussed where representation learning is used. • Various other models are used for re[r] ...
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Proceedings of the 2nd Workshop on Representation Learning for NLP
... on Representation Learning for NLP (RepL4NLP), held on August 3, 2017 and hosted by the 55th Annual Meeting of the Association for Computational Linguistics (ACL) in Vancouver, ...
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Unsupervised Cross Lingual Representation Learning
... lingual representation learning fall into the cate- gory of mapping-based approaches (Ruder et ...joint learning, data augmentation, or ...cross-lingual learning, while so far unexplored, can ...
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Improved Word Representation Learning with Sememes
... There have been a lot of researches for learn- ing word representations, among which word2vec (Mikolov et al., 2013) achieves a nice balance be- tween effectiveness and efficiency. In word2vec, each word corresponds to ...
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Kernel-Based Multilayer Extreme Learning Machines for Representation Learning
... Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the ...
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State representation learning with recurrent capsule networks
... State representation learning with recurrent capsule ...Perception, Learning, and Control, NeurIPS 2018 - 32nd Conference on Neural Information Processing Systems, 07 Dec 2018, Montreal, ...
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Sequence-to-sequence modeling for graph representation learning
... We now discuss supervised methods that learn graph representations for the graph classification task. The representations obtained by these approaches are tailored for a supervised task and are not based solely on the ...
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Lightly supervised Representation Learning with Global Interpretability
... robust learning ap- proaches proposed in representation ...this representation-based ap- proach outperforms three other state-of-the- art bootstrapping approaches on two datasets: CoNLL-2003 and ...
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Proceedings of the 1st Workshop on Representation Learning for NLP
... on Representation Learning for NLP (RepL4NLP), held on August 11, 2016 and hosted by the 54th Annual Meeting of the Association for Computational Linguistics (ACL) in Berlin, ...
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Robust Representation Learning of Biomedical Names
... the representation learning process as a synonym prediction task, with context and con- ceptual losses acting as regularizers, preventing two synonyms from collapsing into semantically meaningless ...
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Exploring Representation Learning Approaches to Domain Adaptation
... Our representation learning approach to domain adaptation yields state-of-the-art results in POS tagging ...current representation learning tech- niques we use are unsupervised, meaning that ...
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Proceedings of The Third Workshop on Representation Learning for NLP
... on Representation Learning for NLP (RepL4NLP) takes place on Friday, July 20, 2018 in Melbourne, Australia, immediately following the 56th Annual Meeting of the Association for Computational Linguistics ...
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Unsupervised Cross Domain Word Representation Learning
... word representation learning methods are bound to a single ...for learning domain-specific word representa- tions that accurately capture the domain- specific aspects of word ...
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Representation Learning for Text level Discourse Parsing
... Discriminative learning of latent features for discourse processing can be viewed as a form of representation learning (Bengio et ...Deep Learning, such approaches have recently been applied ...
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Continual State Representation Learning for Reinforcement Learning using Generative Replay
... of learning over extended periods of time in the real world is a long standing challenge of Reinforcement Learning (RL) research, with direct applications in ...State Representation Learning ...
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Learning the Curriculum with Bayesian Optimization for Task Specific Word Representation Learning
... animal learning (Kail, 1990; Skinner, ...curriculum learning to create improved word ...the representation learning model reads the cor- ...of representation learning ...
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SURREAL: Subgraph Robust Representation Learning
... recent representation learning algorithms (Perozzi et ...baseline representation learning methods strive to preserve similarities among nodes in a single graph, they fail to maintain ...
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Biased Representation Learning for Domain Adaptation
... tation learning, and demonstrate that it is computa- tionally intractable in ...for representation learning to be flexible enough to incorporate the intuitions and knowledge of hu- man experts, to ...
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Soft Representation Learning for Sparse Transfer
... feature representation of shared space in ASP and TARS, in two- and three-source settings respec- ...Zero-shot Learning Lastly, in Table 2, we test zero-shot learning with two target tasks, CNLI and ...
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Pretraining Methods for Dialog Context Representation Learning
... than learning representations that are useful for just the pretraining objective, or for a single downstream task, the learned representa- tions are general and beneficial for multiple ...a representation ...
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