[PDF] Top 20 Multiplicative Multitask Feature Learning
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Multiplicative Multitask Feature Learning
... Research efforts have been devoted to various MultiTask Feature Learning (MTFL) algo- rithms. One direction of these works directly learns the dependencies among tasks, either by modeling the ... See full document
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Modelling the interplay of metaphor and emotion through multitask learning
... Following established work on MTL (Caruana, 1993), we first experiment with hard parameter sharing. In this setting, the architecture shares the word embeddings and lower Bi-LSTM layers be- tween the two tasks, as shown ... See full document
12
Task Clustering and Gating for Bayesian Multitask Learning
... that feature two hidden units with hyperbolic tangents as transfer functions, and linear output ...task learning method (training a separate neural network for each ...non-Bayesian multitask ... See full document
17
Geolocation with Attention Based Multitask Learning Models
... Pre-processing and feature selection We pre- process the text by converting it to lowercase, re- moving URLs and stop-words. We reduce num- bers to 0, except for those appearing in mentions (e.g., @abc123). In ... See full document
7
Incremental and Multi-Task Learning Strategies for Coarse-To-Fine Semantic Segmentation
... the feature extraction level, the model will be fine-tuned to learn different levels of semantic labeling at the same ...to multitask learning [2–4], where multiple tasks are solved at the same time, ... See full document
16
Semi supervised Multitask Learning for Sequence Labeling
... Accurate and efficient sequence labeling mod- els have a wide range of applications, including named entity recognition (NER), part-of-speech (POS) tagging, error detection and shallow pars- ing. Specialised approaches ... See full document
10
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
... We extend the L2-regularized logistic regression framework presented above to incor- porate multiple tasks. We specifically chose L2 regularization over other regularization frameworks (e.g., L1) since many factors ... See full document
23
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 ...a feature engi- neering perspective, by modeling a comment in the ... See full document
12
Effective shared representations with Multitask Learning for Community Question Answering
... 5. The rank feature is provided in the SemEval dataset and describes the position of the ques- tions/comments in the search engine output. Training: we trained our networks using SGD with shuffled mini-batches ... See full document
7
Cross lingual complex word identification with multitask learning
... Cross-lingual learning is the problem of training a model on a given language, and applying it to an- other (unseen) ...or feature sets which can be successful across ... See full document
9
The Benefit of Multitask Representation Learning
... multi-task learning in terms of the spectrum of the data covariance operator and the effective input ...the feature map is constrained by a bound on, say, the trace norm of the associated ... See full document
32
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances
... of-the-art learning algorithms (all for regression), and with different groups of features (the so called “black-box” and “glass-box” feature ...supervised learning set- ting) the similarity of ... See full document
11
Global hydro-climatic biomes identified via multitask learning
... problem. In our framework, we define the number of clusters by using a data-driven approach. In our analysis, we choose not to use information from any predefined number of veg- etation and/or climate classes existing in ... See full document
15
Multitask Learning for Mental Health Conditions with Limited Social Media Data
... These differences in AUC are significant at p = 0.05 according to bootstrap sampling tests with 5000 samples. The wide difference between MTL and STL can be explained in part by the increased feature set size – ... See full document
11
Adversarial Multitask Learning for Joint Multi Feature and Multi Dialect Morphological Modeling
... use multitask learning architectures in several configurations for cross-dialectal ...cross-dialectal multitask learning model, and whether mapping the various pretrained word embedding spaces ... See full document
12
N Best Reranking by Multitask Learning
... We can also observe the feature growth rate (Ta- ble 1). This is the number of new features intro- duced when an additional N-best list is seen. It is important to note that on average, 2599 new fea- tures are ... See full document
9
Confidence Weighted Multitask Learning
... batch learning techniques: multitask feature learn- ing (MTFL) (Argyriou, Evgeniou, and Pontil 2006) and trace-norm regularized MTL (TRML) (Zhou, Chen, and Ye 2011); 2) three online learning ... See full document
8
Feature positive and feature negative learning in honey bees
... The success rate in feature problems was quite low in comparison to that reported for simple discrimination problems and in our positive experimental control. Although performance may improve with more trials in ... See full document
6
On geometric fractional calculus
... This can be shown easily either starting from the definition or by applying the natural logarithm to both sides in the definition and taking the limit to both sides and making use of that f (x) is d- ifferentiable by the ... See full document
14
Experimental Support for a Categorical Compositional Distributional Model of Meaning
... existing multiplicative model, and exploiting the aforementioned feature that the categorical model can be built “on top of” existing lexical distributional models, we used the parame- ters described by ... See full document
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