Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Caglar Gulcehre"'
Autor:
Tom Schaul, David Silver, James Molloy, Junhyuk Oh, Katrina McKinney, Oriol Vinyals, David H. Choi, Junyoung Chung, Tobias Pohlen, Dani Yogatama, Tobias Pfaff, Demis Hassabis, Michael Mathieu, Dan Horgan, Ivo Danihelka, Igor Babuschkin, Dario Wünsch, Tom Le Paine, Yury Sulsky, Wojciech Marian Czarnecki, Rémi Leblond, Ziyu Wang, Andrew Dudzik, Trevor Cai, Chris Apps, Yuhuai Wu, David Budden, Valentin Dalibard, Timo Ewalds, Oliver Smith, John P. Agapiou, Aja Huang, Roman Ring, Petko Georgiev, Max Jaderberg, Koray Kavukcuoglu, Alexander Vezhnevets, Caglar Gulcehre, Manuel Kroiss, Laurent Sifre, Richard E. Powell, Timothy P. Lillicrap
Publikováno v:
Nature. 575:350-354
Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence re
Publikováno v:
Neural Computation. 30:857-884
We extend the neural Turing machine (NTM) model into a dynamic neural Turing machine (D-NTM) by introducing trainable address vectors. This addressing scheme maintains for each memory cell two separate vectors, content and address vectors. This allow
Publikováno v:
Computer Speech & Language. 45:137-148
Recent advances in end-to-end neural machine translation models have achieved promising results on high-resource language pairs such as En -> Fr and En -> De. One of the major factor behind these successes is the availability of high quality parallel
Autor:
Max Tegmark, John Peurifoy, Marin Soljacic, Li Jing, Yichen Shen, Caglar Gulcehre, Yoshua Bengio
Publikováno v:
Neural computation. 31(4)
We present a novel recurrent neural network (RNN)–based model that combines the remembering ability of unitary evolution RNNs with the ability of gated RNNs to effectively forget redundant or irrelevant information in its memory. We achieve this by
Autor:
Tong Wang, Xingdi Yuan, Caglar Gulcehre, Alessandro Sordoni, Adam Trischler, Sandeep Subramanian, Philip Bachman, Saizheng Zhang
Publikováno v:
Rep4NLP@ACL
We propose a recurrent neural model that generates natural-language questions from documents, conditioned on answers. We show how to train the model using a combination of supervised and reinforcement learning. After teacher forcing for standard maxi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b21ecb94f3bd5b7a53792e793ee451c3
http://arxiv.org/abs/1705.02012
http://arxiv.org/abs/1705.02012
Publikováno v:
Rep4NLP@ACL
We investigate the integration of a planning mechanism into an encoder-decoder architecture with attention. We develop a model that can plan ahead when it computes alignments between the source and target sequences not only for a single time-step but
Publikováno v:
IJCNN
Stochastic gradient algorithms are the main focus of large-scale optimization problems and led to important successes in the recent advancement of the deep learning algorithms. The convergence of SGD depends on the careful choice of learning rate and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::415f49aca507cf3100d8980af2f087f3
Publikováno v:
ACL (1)
The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both the traditional count-based and the deep learning models. We propose a novel way to deal with the rare and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d511039502a020fa0ceeb566bd664ade
http://arxiv.org/abs/1603.08148
http://arxiv.org/abs/1603.08148
Publikováno v:
CoNLL
In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7529a765525c9159f4fbe497c386fc9d
Autor:
Aaron Courville, Yoshua Bengio, Alberto Garcia-Duran, Caglar Gulcehre, Sungjin Ahn, Iulian Vlad Serban, Sarath Chandar
Publikováno v:
ACL (1)
Over the past decade, large-scale supervised learning corpora have enabled machine learning researchers to make substantial advances. However, to this date, there are no large-scale question-answer corpora available. In this paper we present the 30M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee701b34c9cdc5569b45ce0e14e17efa