[PDF] Top 20 Identifying beneficial task relations for multi task learning in deep neural networks
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Identifying beneficial task relations for multi task learning in deep neural networks
... with deep recurrent neural ...single task architectures are reused in the multi-task set- up (no additional tuning), which makes predict- ing gains ...90 task configurations and ... See full document
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Representation Learning Using Multi Task Deep Neural Networks for Semantic Classification and Information Retrieval
... between multi-task learning and neu- ral nets is quite natural; the general idea dates back to (Caruana, ...that multi-task models of- ten exhibit mixed results ... See full document
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Multi Task Deep Neural Networks for Natural Language Understanding
... one task can benefit other ...representation learning using deep neu- ral networks (DNNs) (Collobert et ...supervised learning of DNNs re- quires large amounts of task-specific ... See full document
10
Multi Task Networks with Universe, Group, and Task Feature Learning
... that task structure is usually un- clear, Evgeniou and Pontil (2004) extended sup- port vector machines for single-task learning in a multi-task scenario by penalizing models if they ... See full document
11
Impact of Earnings per Share on Market Price of Share with Special Reference to Selected Companies Listed on NSE
... as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the ...Various deep learning architectures such as deep neural ... See full document
5
YNUWB at SemEval 2019 Task 6: K max pooling CNN with average meta embedding for identifying offensive language
... machine learning methods to obtain tagged data from different Twitter accounts in an inex- pensive way, and to learn the binary classifier- s of the “racist” and “nonracist” tags (Kwok and Wang, ...al ... See full document
5
TECHSSN at SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
... uses multi-level classification ...a multi-branch 2D CNN classi- fier with Google’s pre-trained Word2Vec em- bedding and the second level of classification by string matching technique supported by of- ... See full document
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Single Satellite Imagery Simultaneous Super resolution and Colorization using Multi task Deep Neural Networks
... proposed multi-task deep learning approach is superior to the state-of-the-art methods, where image SR and colorization can be accomplished simultane- ously and ... See full document
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Multi Task Spatiotemporal Neural Networks for Structured Surface Reconstruction
... in deep networks for video analysis, where the frames of video can be viewed as similar to our tomographic ...apply deep networks to video applica- tions focus on efficient ways to combine ... See full document
10
Deep Learning as a Frontier of Machine Learning: A Review
... Deep neural network is a variant of multilayer feed-forward artificial neural ...the deep neural ...many neural network models and second, the issue of computation ...in ... See full document
9
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning
... shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, which is pre-trained on a large text cor- pus to produce a contextualized word ...classification ... See full document
6
Learning to recognise named entities in tweets by exploiting weakly labelled data
... ence between the expected and the actual value) changes with a change in weight or bias value. The gradient is computed from the output layer (i.e., the current word) and is propagated back to the first layer (i.e., the ... See full document
11
Proceedings of the 13th International Workshop on Semantic Evaluation
... of task-independent language understanding: building machine learning models that can learn to do most of the hard work of language understanding before they see a single example of the language ... See full document
46
Neural Machine Translation for Bilingually Scarce Scenarios: a Deep Multi Task Learning Approach
... The configuration of models is as follows. The en- coders and decoders make use of GRU units with 400 hidden dimensions, and the attention compo- nent has 200 dimensions. For training, we used Adam algorithm (Kingma and ... See full document
10
Machine Learning Perspectives for Dental Imaging
... supervised learning classification algorithm and also one of the most widely used non-parametric pattern classification method, which reduce the concern of complexity of probability ... See full document
5
Linguistic representations in multi task neural networks for ellipsis resolution
... These classification results provide an indica- tion that, in some sense, the networks are in- deed noticing whether the wh-words appear in a sluice or not. We suggest further that this is re- flected quite ... See full document
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Improving Robustness of Neural Machine Translation with Multi task Learning
... Denoising text: Sakaguchi et al. (2017) pro- poses semi-character level recurrent neural net- work (scRNN) to correct words with scrambling characters. Each word is represented as a vector with elements ... See full document
7
Twitter Demographic Classification Using Deep Multi modal Multi task Learning
... α = sof tmax(W (2) tanh(W (1) M + b (1) ) + b (2) ) (7) where α ∈ IR 1 × d . We multiply each of the fea- ture vectors by their corresponding α value to get a weighted feature representation. These weighted ... See full document
6
Deep Multi Task Learning with Shared Memory for Text Classification
... specific task, we plot and observe the evolving activation of fusion gates through time, which controls signals flowing from a shared exter- nal memory to task-specific output, to understand the behaviour ... See full document
10
Object Detection in an Image using Deep Learning
... supported deep learning is a vital application in deep learning technology, that is characterised by its robust capability of feature learning and have illusation compared with the ... See full document
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