[PDF] Top 20 A survey of multi-task learning methods in chemoinformatics
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A survey of multi-task learning methods in chemoinformatics
... time-consuming task, and there is a strong interest in how to make the best use of all available ...Thus learning several ADMETox properties simultaneously can result in better ...different methods ... See full document
11
Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey
... In general, a distributed computing system is defined as a group of processors connected via a high speed network, which chains the execution of distributed applications [1]. Distributed computing systems can be divided ... See full document
7
Multi-Task Learning using Dynamic Task Weighting for Conversational Question Answering
... MLT methods when integrated into the BERT ConvQA model, we choose QuAC (Choi et ...a multi-turn dataset where the questions and answers simulate ...main task but it also provides other auxiliary ... See full document
10
A review on multi-task metric learning
... proposed multi-task maximally collapsing metric learning to solve the person re-identification over camera ...a multi-task learning approach for this ...the ... See full document
23
Learning Multi-Task Communication with Message Passing for Sequence Learning
... non-neural multi-task learning methods have also been proposed to model the relationships between ...structured multi-task problem over a given ...complex learning ... See full document
8
A Multi task Approach to Learning Multilingual Representations
... to learning multilingual embeddings is to train a multilingual word embedding model that is then used to derive representations for sentences and documents by composition (Hermann and Blun- som, ...thorough ... See full document
7
Bayesian multi-task learning for decoding multi-subject neuroimaging data
... a multi-task learning (MTL) ...MTL methods produce higher decoding accuracy and more consistent discriminative activity patterns than currently used tech- ...for multi-subject decoding ... See full document
14
Deep learning for multi task plant phenotyping
... Some methods do exist for automatically detect- ing heading and flowering in wheat - [21] uses a bag-of- visual-words approach to identify growth stages in field- grown ... See full document
9
Imitation Learning: A Survey of Learning Methods
... Imitation learning techniques aim to mimic human behavior in a given ...(a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and ... See full document
35
Multi Task Networks with Universe, Group, and Task Feature Learning
... present methods for multi-task learning that take advantage of natural groupings of re- lated ...tasks. Task groups may be defined along known properties of the tasks, such as ... See full document
11
Multi Task Learning for Coherence Modeling
... Paragraph sequence (PARSEQ). Lai and Tetreault (2018) implemented a hierarchical neural network consisting of three LSTMs to generate sentence, paragraph and document representations. The network’s architecture is ... See full document
11
Concept Classification with Bayesian Multi task Learning
... This result bears a strong resemblance to the re- cent work of Just et al. (2010). The authors con- ducted a factor analysis of fMRI brain activation in response to presentations of written words of differ- ent ... See full document
8
SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
... As one of the fundamental tasks in NLP, se- quence labeling has been studied for years. Before the blooming of neural network methods, hand- crafted features were widely used in traditional approaches like CRFs, ... See full document
11
Support Vector Machines and Multi-Task Learning
... In many practical situations a number of statistical models has to be obtained from data for different problems. In the literature each one of these problems is called a task. A classical example used in [1, 4] is ... See full document
80
Multi Domain Adaptation for SMT Using Multi Task Learning
... jointly learning these tasks led to superior ...the multi-domain learning and ...distinct task. Simianer et al. (2012) proposed dis- tributed stochastic learning with feature selection ... See full document
11
Union Support Recovery in Multi-task Learning
... penalization methods have better theoretical properties in the presence of the design matrix, especially when the design matrix is far from satisfying the irrepresentable condition (Zhao and Yu, ... See full document
21
Latent Multi-Task Architecture Learning
... for learning which parts of multi-task models to share, with a small set of additional parameters to learn, can achieve significant and consistent improvements over strong baseline ... See full document
8
Multi task Learning for Natural Language Generation in Task Oriented Dialogue
... Natural Language Generation (NLG) is the fi- nal procedure in the pipeline of task-oriented di- alogues. As the result of NLG is directly fac- ing users, its readability and informativeness have a direct impact on ... See full document
6
Multi Task Learning for Improved Discriminative Training in SMT
... ranking methods use a smoothed sentence-wise BLEU+1 score (Nakov et ...Our multi-task learning experiments are based on pairwise ranking perceptrons that differ in their objective, ... See full document
9
Multi Task Active Learning for Linguistic Annotations
... Supervised machine learning methods have success- fully been applied to many NLP tasks in the last few decades. These techniques have demonstrated their superiority over both hand-crafted rules and unsu- ... See full document
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