[PDF] Top 20 Inductive learning in Shared Neural Multi-Spaces
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Inductive learning in Shared Neural Multi-Spaces
... of inductive momentum between predicates connected to positive and negative ...the Shared NeMuS data structure can be used as a suitable structure for inductive logic programming; the Herbrand Base ... See full document
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Multi source Neural Automatic Post Editing: FBK’s participation in the WMT 2017 APE shared task
... Jointly learning from both source and transla- tion has been previously proved to be effective in (B´echara et ...the neural paradigm, recent prior work mostly fo- cuses on single-source systems (Pal et ... See full document
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Simple, Efficient and Convenient Decentralized Multi-Task Learning for Neural Networks
... for multi-task learning focus on a local setup, rather than a distributed one [Rud17], but some decentralized solutions ...decentralized multi-task learning algorithm limited to linear models, ... See full document
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Multi Task Learning for Speaker Role Adaptation in Neural Conversation Models
... Most relevant to the present work, (Li et al., 2016b) propose employing speaker embeddings to encode persona information and allow conversa- tion data of similar users on social media to be shared for model ... See full document
10
Lexicon information in neural sentiment analysis: a multi task learning approach
... that multi- task learning (Caruana, 1993; Collobert et ...proposed multi-task model shares the lower layers in a multi-layer neural network, while allowing the higher layers to adapt to ... See full document
12
Multi Module Recurrent Neural Networks with Transfer Learning
... The VUA Metaphor Corpus has been previously used to automatically predict the metaphoricity of verbs. In the baseline paper (Klebanov et al., 2016) authors explore multiple feature spaces, based on VerbNet and ... See full document
5
Deep Multi Task Learning with Shared Memory for Text Classification
... Recursive Neural Network with parse trees (Socher et ...Recursive Neural Tensor Network with tensor-based feature function and parse trees (Socher et ...Convolutional Neural Network with dynamic ... See full document
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Multi-task learning deep neural networks for speech feature denoising
... the multi-task learning deep neural networks (MTL-DNN) to solve the speech denoising task in feature ...jointly learning multiple interactive tasks using a shared ...In ... See full document
5
Improving Robustness of Neural Machine Translation with Multi task Learning
... source text but also translate it. We design a strat- egy for synthesizing data triplets for this architec- ture. Our model could be viewed as a combina- tion of denoising source text and domain adap- tation, both of ... See full document
7
Multi Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces
... for learning similarities between tasks enforce a clustering of tasks (Evgeniou et ...a shared prior (Yu et ...sets. Multi-task learning with neural networks Re- cent work in ... See full document
11
Learning Label Structures with Neural Networks for Multi-label Classification
... label spaces including unseen ...zero-shot learning (ZSL). Traditional classification tasks aim at learning mapping functions from training instances to the set of labels in the training set, and ... See full document
130
Multi Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
... current neural network, called a decoder, then gener- ates a target sequence again of variable length start- ing from the context ...in learning a complex mapping between an arbitrary long sentence and a ... See full document
10
Learning Package by Means of the Inductive Teaching with Group Process
... that inductive teaching was the teaching procedure from details to the main topics, or teaching from observing, testing, or comparing from the information provided and then concluding the principles or rules from ... See full document
6
Autoregressive generative models and multi-task learning with convolutional neural networks
... Contributions are as follows: 1) I show that musical instrument synthe- sizers can be learned end-to-end based on raw audio and a binary note rep- resentation, with minimal training data. Multiple instruments can be ... See full document
194
NITE: A Neural Inductive Teaching Framework for Domain Specific NER
... Multiple Instance Learning is an effective train- ing method that can help to train a supervised model to alleviate the wrong label problem (Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012). ... See full document
6
A Comparison of Incremental Case Based Reasoning and Inductive Learning
... This paper focuses on problems where the reuse of old solutions seems appropriate but the conventional CBR methodology is not adequate because a complete description of the new problem is not available to trigger case ... See full document
6
Spaces for learners and learning: evaluating the impact of technology-rich learning spaces
... to learning that models curriculum change, learning and teaching delivery - often expressed in the mission and aims of institutional learning and teaching ...these spaces exist in the United ... See full document
11
Quotient inductive-inductive types
... Our categories of algebras are complete. This is helpful for the metatheory of QIITs, as demonstrated by the proof of initiality being equivalent to section induction (Theorem 31), justifying elimination principles. Of ... See full document
17
Quotient inductive inductive types
... Recall from Definition 13 that the category of algebras C .(F, G) for a constructor specification (F, G) on a complete category C has “dependent (F, G)-dialgebras” as objects, and maps that commute with the dialgebra ... See full document
18
On Multi Normed Linear Spaces
... From 1989 to 1991, Wayne D. Blizard made a thorough study of multiset theory, real valued multisets and negative membership of the elements of multisets ([1], [2],[3],[4]). K. P. Girish and S. J. John introduced and ... See full document
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