Fetching the paper…
Reading the bibliography…
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements of a sequence.
Speaker-independent isolated word recognition based on emphasized spectral dynamics
Sadaoki Furui · 1986
Earlier work this paper cites.
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
Steven B Davis and Paul Mermelstein · 1990
Earlier work this paper cites.
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1
John S Garofolo, Lori F Lamel, William M Fisher, Jonathan G Fiscus, and David S Pallett · 1993
Earlier work this paper cites.
Neural networks for quaternion-valued function approximation
Paolo Arena, Luigi Fortuna, Luigi Occhipinti, and Maria Gabriella Xibilia · 1994
Earlier work this paper cites.
A quaternary version of the back-propagation algorithm
Tohru Nitta · 1995
Earlier work this paper cites.
Fourier transforms of colour images using quaternion or hypercomplex, numbers
Stephen John Sangwine · 1996
Earlier work this paper cites.
Multilayer perceptrons to approximate quaternion valued functions
Paolo Arena, Luigi Fortuna, Giovanni Muscato, and Maria Gabriella Xibilia · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
A comparative study of three methods for robot kinematics
Nicholas A Aspragathos and John K Dimitros · 1998
Earlier work this paper cites.
Complex recurrent neural network for computing the inverse and pseudo-inverse of the complex matrix
Jingyan Song and Yeung Yam · 1998
Earlier work this paper cites.
Color image processing by using binary quaternion-moment-preserving thresholding technique
Soo-Chang Pei and Ching-Min Cheng · 1999
Earlier work this paper cites.
Recurrent neural networks
Larry R. Medsker and Lakhmi J. Jain · 2001
Earlier work this paper cites.
Weighted finite-state transducers in speech recognition
Mehryar Mohri, Fernando Pereira, and Michael Riley · 2001
Earlier work this paper cites.
Quaternion neural network and its application
Teijiro Isokawa, Tomoaki Kusakabe, Nobuyuki Matsui, and Ferdinand Peper · 2003
Earlier work this paper cites.
Trajectory modeling based on hmms with the explicit relationship between static and dynamic features
Keiichi Tokuda, Heiga Zen, and Tadashi Kitamura · 2003
Earlier work this paper cites.
A new scheme for color night vision by quaternion neural network
Hiromi Kusamichi, Teijiro Isokawa, Nobuyuki Matsui, Yuzo Ogawa, and Kazuaki Maeda · 2004
Earlier work this paper cites.
Quaternion neural network with geometrical operators
Nobuyuki Matsui, Teijiro Isokawa, Hiromi Kusamichi, Ferdinand Peper, and Haruhiko Nishimura · 2004
Earlier work this paper cites.
Quaternionic neural networks: Fundamental properties and applications
Teijiro Isokawa, Nobuyuki Matsui, and Haruhiko Nishimura · 2009
Cited alongside, same era.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
Cited alongside, same era.
The kaldi speech recognition toolkit
Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukas Burget, Ondrej Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlicek, Yanmin Qian, Petr Schwarz, Jan Silovsky, Georg Stemmer, and Karel Vesely · 2011
Cited alongside, same era.
Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence
Akira Hirose and Shotaro Yoshida · 2012
Cited alongside, same era.
Global stability of complex-valued recurrent neural networks with time-delays
Jin Hu and Jun Wang · 2012
Cited alongside, same era.
Hybrid speech recognition with deep bidirectional lstm
Purely sequence-trained neural networks for asr based on lattice-free mmi
Daniel Povey, Vijayaditya Peddinti, Daniel Galvez, Pegah Ghahremani, Vimal Manohar, Xingyu Na, Yiming Wang, and Sanjeev Khudanpur · 2016
Later among the works it cites.
High Dimensional Neurocomputing
Bipin Kumar Tripathi · 2016
Later among the works it cites.
A mathematical motivation for complex-valued convolutional networks
Mark Tygert, Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, and Arthur Szlam · 2016
Later among the works it cites.
Full-capacity unitary recurrent neural networks
Scott Wisdom, Thomas Powers, John Hershey, Jonathan Le Roux, and Les Atlas · 2016
Later among the works it cites.
Feed forward neural network with random quaternionic neurons
Toshifumi Minemoto, Teijiro Isokawa, Haruhiko Nishimura, and Nobuyuki Matsui · 2017
Later among the works it cites.
Quaternion denoising encoder-decoder for theme identification of telephone conversations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed · 2013
Cited alongside, same era.
Speech recognition with deep recurrent neural networks
Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton · 2013
Cited alongside, same era.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
Cited alongside, same era.
Small-footprint keyword spotting using deep neural networks
G. Chen, C. Parada, and G. Heigold · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
Cited alongside, same era.
Deep recurrent neural networks for acoustic modelling
William Chan and Ian Lane · 2015
Cited alongside, same era.
Titouan Parcollet, Morchid Mohamed, and Georges Linarès · 2017
Later among the works it cites.
Deep quaternion neural networks for spoken language understanding
Titouan Parcollet, Mohamed Morchid, and Georges Linares · 2017
Later among the works it cites.
Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
Later among the works it cites.
Chiheb Trabelsi, Olexa Bilaniuk, Dmitriy Serdyuk, Sandeep Subramanian, João Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, and Christopher J Pal · 2017
Later among the works it cites.
Learning alogrithms in quaternion neural networks using ghr calculus
D Xu, L Zhang, and H Zhang · 2017
Later among the works it cites.
Manifoldnet: A deep network framework for manifold-valued data
Rudrasis Chakraborty, Jose Bouza, Jonathan Manton, and Baba C. Vemuri · 2018
Closest in time.
State-of-the-art speech recognition with sequence-to-sequence models
Chung-Cheng Chiu, Tara N Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J Weiss, Kanishka Rao, Ekaterina Gonina, et al · 2018
Closest in time.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties, 2018
Alexis Conneau, German Kruszewski, Guillaume Lample, Lo ï · 2018
Closest in time.
Deep quaternion networks
Chase J Gaudet and Anthony S Maida · 2018
Closest in time.
Parsimonious memory unit for recurrent neural networks with application to natural language processing
Mohamed Morchid · 2018
Closest in time.
Quaternion convolutional neural networks for end-to-end automatic speech recognition
Titouan Parcollet, Ying Zhang, Mohamed Morchid, Chiheb Trabelsi, Georges Linarès, Renato de Mori, and Yoshua Bengio · 2018
Closest in time.