multitask learning
Confidence Weighted Multitask Learning
... active learning (OAL) addresses this concern by let- ting the learner decide whether to request the true label of the current instance or ...the multitask learn- ing setting, one can further reduce the ...
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Recurrent Multitask-Learning for Irregular Clinical Time Series Forecasting
... machine learning, there is a surging interest to tackle these problems with predictive ...like multitask learning and parameterizing missing variables to help the model overcome data sparsity and ...
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Modelling the interplay of metaphor and emotion through multitask learning
... several multitask learning architec- tures for this purpose, involving both hard and soft parameter ...joint learning and our models advance the state of the art in both of these ...
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Cross lingual complex word identification with multitask learning
... We approach the 2018 Shared Task on Com- plex Word Identification by leveraging a cross- lingual multitask learning approach. Our method is highly language agnostic, as evi- denced by the ability of our ...
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Online Multitask Learning for Machine Translation Quality Estimation
... and multitask learning, our so- lution unifies the two paradigms in a single on- line multitask ...online multitask learning methods that only operate in classification ...
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Smart Task Orderings for Active Online Multitask Learning
... Multitask learning (MTL) investigates offline mode ma- chine learning systems that can learn a set of related tasks in one batch [1], yet in real world applications a bunch of tasks often arrive over ...
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Global hydro-climatic biomes identified via multitask learning
... Here, we introduce for the first time (to the best of our knowledge) a data-driven approach that aims to quantify the response of vegetation to local climate variables in a su- pervised setting at a global scale and use ...
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Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances
... We investigate the problem of predicting the quality of automatic speech recognition (ASR) output under the following rigid con- straints: i) reference transcriptions are not available, ii) confidence information about ...
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Large scale Multitask Learning for Machine Translation Quality Estimation
... Multitask learning has been proven a useful technique in a number of Natural Language Processing applications where data is scarce and naturally ...the learning of tasks in parallel while using a ...
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Discovering multi-purpose modules through deep multitask learning
... simultaneously learning the parameters of the modules themselves, and the complete optimization is performed end-to-end using gradient ...deep multitask learning approaches on Omniglot ...
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Deep Multitask Learning for Semantic Dependency Parsing
... Multitask learning in ...joint learning to re- place pipelines, motivated by concerns about cas- cading ...to multitask learning. Successes in multitask learning have been ...
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Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
... chine learning to build models that help healthcare providers improve patient ...of learning a risk stratification model for predicting which patients are at risk of acquiring a Clostridium difficile ...
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Adaptively Scheduled Multitask Learning: The Case of Low Resource Neural Machine Translation
... the multitask ar- chitecture. In standard multitask learning w i (k) is set to 1, assuming all of the tasks and their train- ing instances have the same ...to learning the best inductive ...
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Joint Multitask Learning for Community Question Answering Using Task Specific Embeddings
... structured learning problems, there is a lot of research trying to exploit the correlations between the comments in a question–comment ...the learning process using global ...perform multitask ...
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Adversarial Multitask Learning for Joint Multi Feature and Multi Dialect Morphological Modeling
... of multitask learning and adversarial training to address morpho- logical richness and dialectal variations in the context of full morphological ...use multitask learning for joint ...
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When is multitask learning effective? Semantic sequence prediction under varying data conditions
... Multitask learning has been applied suc- cessfully to a range of tasks, mostly mor- phosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to deter- ...
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DCASE 2019 Task 2: Multitask Learning, Semi-supervised Learning and Model Ensemble with Noisy Data for Audio Tagging
... label. Learning with soft pseudo labels is performed in parallel with multitask ...with multitask learning (Table 1 #4) using Snapshot Ensembles [10] and 5-fold cross validation (CV) averaging ...
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Cross lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
... deep learning have prompted a surge of interest in the applica- tion of multitask and transfer learning to NLP ...and multitask learning (MTL) to the identification of Multiword ...
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Task Clustering and Gating for Bayesian Multitask Learning
... task learning method (training a separate neural network for each ...non-Bayesian multitask learning: in this intermediate model we applied the same network structure as in the Bayesian ...
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N Best Reranking by Multitask Learning
... the multitask learning algorithms? We found that that two kinds of features are usually selected: one is general features that are not lexicalized, such as “count of phrases”, “count of dele- ...
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