[PDF] Top 20 The Benefit of Multitask Representation Learning
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The Benefit of Multitask Representation Learning
... Multitask learning (MTL) can be characterized as the problem of learning multiple tasks jointly, as opposed to learning each task in ...few. Multitask learning algorithms which ... See full document
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Multitask Learning for Mental Health Conditions with Limited Social Media Data
... all tasks. An additional per-task hidden layer is used to give the model flexibility to map from the task-agnostic representation to a task-specific one. Each hidden layer uses a rectified linear unit as ... See full document
11
Large scale Multitask Learning for Machine Translation Quality Estimation
... trend. Multitask models lead to further improvements, particularly visible for post-editor 3 (the one with less training data), where the crosslin- gual multitask learning model reaches ... See full document
10
Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach
... Automated patient risk stratification, based on the contents of the patient’s EHR, can serve several purposes. Firstly, risk-stratification models can help clinicians match high-risk patients with the appropriate ... See full document
23
Semi supervised Multitask Learning for Sequence Labeling
... learner texts, named entity recognition, chunking and POS-tagging. We found that the additional language modeling objective provided consistent performance improvements on every benchmark. The largest benefit from ... See full document
10
Geolocation with Attention Based Multitask Learning Models
... we use a small context window size of 5 words. We trained our embeddings on the training sets of each corpus. As we are interested in poten- tially rare geographically informative words, we use the skip-gram model, which ... See full document
7
Tied Multitask Learning for Neural Speech Translation
... typical multitask setup (Weiss et ...of representation, at least at the level of phonemes if not syntax or ...titask learning with sequence-to-sequence models, in which the decoder of the second task ... See full document
10
Global hydro-climatic biomes identified via multitask learning
... low-dimensional representation among the ...the learning process since the weight matrix and the matrix which cap- tures the shared low-dimensional representation are learned ...by learning ... See full document
15
Radar Target Recognition using Salient Keypoint Descriptors and Multitask Sparse Representation
... method outperforms the remaining ones under the different considered number of dictionary atoms. Additionally, comparing the matching and the MSRC to recognize the MSKD, it is observed that with the decreasing number of ... See full document
15
Cross lingual Transfer Learning and Multitask Learning for Capturing Multiword Expressions
... transfer learning has been extensively ex- plored in the context of representation learning where monolingual spaces are mapped into a com- mon embedding space through methods like retro- fitting ... See full document
7
Deep Multitask Learning for Semantic Dependency Parsing
... MRS) representation comes from DeepBank (Flickinger et ...tures) representation is extracted from the Enju Treebank, which consists of automatic parses from the Enju HPSG parser (Miyao, ...Dependencies) ... See full document
12
Modelling the interplay of metaphor and emotion through multitask learning
... several multitask learning architec- tures for this purpose, involving both hard and soft parameter ...mutually benefit from joint learning and our models advance the state of the art in both ... See full document
12
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances
... In classification, the class boundary is defined a priori, according to an arbitrary threshold τ set on the WER of the instances: those with a W ER ≤ τ will be considered as positive examples while the others will be ... See full document
11
Effective shared representations with Multitask Learning for Community Question Answering
... by learning the target DNN together with two auxiliary tasks in a mul- titask learning ...global representation for com- ments, new and previous ... See full document
7
N Best Reranking by Multitask Learning
... whether multitask learning extracts novel features, especially those that have low fre- ...feature representation (feature threshold) which only keeps features that occur in more than x N- bests, and ... See full document
9
Task Clustering and Gating for Bayesian Multitask Learning
... Application of our methods on an artificial data set demonstrated that appropriately structured regression problems can benefit significantly both from the multitask learning approach and from task ... See full document
17
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 correlated ... See full document
33
Statistical and Relational Learning for Understanding Enzyme Function
... Starting from the protein primary sequence the name and type of the residue and a multiple alignment profile computed using Blast+ 1 [28] (see also Section 3.2.1 and Table 3.1), are used for building the residue vecto- ... See full document
230
Revisiting Projection-Free Optimization for Strongly Convex Constraint Sets
... becomes equivalent to normalized gradient descent. In this paper, we both 1) show that FW can converge to a neigh- borhood of a global minimum, and 2) derive a convergence rate. (Dunn 1979) extends the analysis of FW to ... See full document
8
Adaptively Scheduled Multitask Learning: The Case of Low Resource Neural Machine Translation
... Neural Machine Translation (NMT), a data- hungry technology, suffers from the lack of bilingual data in low-resource scenarios. Mul- titask learning (MTL) can alleviate this is- sue by injecting inductive biases ... See full document
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