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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho,Bart van Merriënboer,Caglar Gulcehre,Dzmitry Bahdanau,Fethi Bougares,Holger Schwenk,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio +8 more
TLDR
Qualitatively, the proposed RNN Encoder‐Decoder model learns a semantically and syntactically meaningful representation of linguistic phrases.Abstract:
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.read more
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Sequence to Sequence Learning with Neural Networks
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
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Sequence to Sequence Learning with Neural Networks
TL;DR: This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
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A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu,Jimmy Ba,Ryan Kiros,Kyunghyun Cho,Aaron Courville,Ruslan Salakhutdinov,Richard S. Zemel,Yoshua Bengio +7 more
TL;DR: This paper proposed an attention-based model that automatically learns to describe the content of images by focusing on salient objects while generating corresponding words in the output sequence, which achieved state-of-the-art performance on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.
References
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