Multi-task learning
Support Vector Machines and Multi-Task Learning
... the multi-task learning SVRs following the works of [1, ...the multi-task SVMs are described as a variant that solves multiple tasks at once, training one common part of the models to ...
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Taking advantage of sparsity in multi-task learning
... the multi-task learning scenario, in which T can grow, we are able to remove completely the effect of the number of pre- dictor variables in the ...
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Concept Classification with Bayesian Multi task Learning
... Bayesian multi-task learning which imposes a coupling between single-subject ...The multi-task learning algorithm is shown to find regions of interest that are common to all ...
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All in one : multi task learning for rumour verification
... joint multi- task learning ...of task-specific layers. The possible task combinations are shown as dotted lines on Figure 3 that can be present or absent depending on the ...(3) ...
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A multi-task learning CNN for image steganalysis
... a multi-task learning CNN is ...CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary ...
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Union Support Recovery in Multi-task Learning
... In this paper we study multi-task learning in the context of the many Normal means model. This is a simplified model that is often useful for studying the theoretical properties of statistical ...
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Multi task Learning for Natural Language Generation in Task Oriented Dialogue
... In task-oriented dialogues, Natural Language Generation (NLG) is the final and crucial step to produce user-facing system ...novel multi-task learning framework, NLG-LM, for natural language ...
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Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
... sequential learning, whenever a trained model, upon training in a new task, moves abruptly in the space of parameters, effectively “forgetting” the original ...second task, these algorithms add an ...
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Multi Task Learning for Improved Discriminative Training in SMT
... Multi-task learning has been shown to be effective in various applications, including discriminative ...whether multi-task learning depends on a “natu- ral” division of data into ...
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Multi Task Learning of Keyphrase Boundary Classification
... Multi-Task Learning Hard sharing of all hid- den layers was introduced in Caruana (1993), and popularised in NLP by Collobert et al. (2011a). Several variants have been introduced, including hard ...
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Multi Task Learning for Multiple Language Translation
... sequence learning problem such as speech recognition and machine translation where input and output sequences are of variable ...our multi-task learning framework, the parameters of the gated ...
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Adversarial Multi task Learning for Text Classification
... vanilla multi-task learning ...the task-specific layer introduced in the Bi- Channel model can store some private ...adversarial multi-task learning, which is quite useful ...
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Multi-Task Learning for Classification with Dirichlet Process Priors
... of multi-task learning is different from that of meta ...the learning performance ...individual task, or to boost the performance of a new task by transferring domain knowledge ...
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SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
... Multi-task learning (MTL) has been studied recently for sequence ...target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can in- crease the ...
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Multi Domain Adaptation for SMT Using Multi Task Learning
... novel multi-domain adaptation ap- proach for SMT using Multi-Task Learning (MTL), with in-domain models tailored for each specific domain and a general-domain model shared by different ...
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Multi task Learning for Multi modal Emotion Recognition and Sentiment Analysis
... leverage multi-modal and contextual information for pre- dicting sentiment and emotion of an utterance simultaneously in a multi-task learning frame- ...each task in the ...
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Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering
... leverage Multi-Task Learning (MTL) use the static method with unchanged tasks’ weights during the training ...apply Multi-Task Learning in order to improve the effectiveness of ...
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Deep Automated Multi task Learning
... in multi-task learning frameworks proposed in (Liu et ...semi-supervised multi-task frameworks (Ando and Zhang, 2005), to our knowledge there is no work dedicated to using tasks derived ...
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Multi Task Learning for Coherence Modeling
... hierarchical multi-task learn- ing framework for discourse coherence that takes advantage of inductive transfer between two tasks: predicting the GR type of words at the bottom lay- ers of the network and ...
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Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
... Transfer learning (Pan and Yang, 2010) and, in particular, Multi-Task Learning (MTL) (Caruana, 1997) leverage such underlying common links among a group of tasks, while respecting the tasks’ ...
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