Fetching the paper…
Reading the bibliography…
Deep reinforcement learning (deep RL) is a combination of deep learning with reinforcement learning principles to create efficient methods that can learn by interacting with its environment.
S. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences,”
1980
Earlier work this paper cites.
G. Tesauro, “Temporal difference learning and td-gammon,”
1995
Earlier work this paper cites.
S. Singh, D. Litman, M. Kearns, and M. Walker, “Optimizing dialogue management with reinforcement learning: Experiments with the njfun system,”
2002
Earlier work this paper cites.
J. R. Tetreault and D. J. Litman, “A reinforcement learning approach to evaluating state representations in spoken dialogue systems,”
2008
Earlier work this paper cites.
D. Yu, L. Deng, and G. Dahl, “Roles of pre-training and fine-tuning in context-dependent dbn-hmms for real-world speech recognition,” in
2010
Earlier work this paper cites.
F. Abtahi and I. Fasel, “Deep belief nets as function approximators for reinforcement learning,” in
2011
Earlier work this paper cites.
S. Thomas, M. L. Seltzer, K. Church, and H. Hermansky, “Deep neural network features and semi-supervised training for low resource speech recognition,” in
2013
Earlier work this paper cites.
Y. Liu and K. Kirchhoff, “Graph-based semi-supervised acoustic modeling in dnn-based speech recognition,” in
2014
Earlier work this paper cites.
D. Imseng, B. Potard, P. Motlicek, A. Nanchen, and H. Bourlard, “Exploiting un-transcribed foreign data for speech recognition in well-resourced languages,” in
2014
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski
2015
Earlier work this paper cites.
C. W. Anderson, M. Lee, and D. L. Elliott, “Faster reinforcement learning after pretraining deep networks to predict state dynamics,” in
2015
Cited alongside, same era.
B. McFee, C. Raffel, D. Liang, D. P. Ellis, M. McVicar, E. Battenberg, and O. Nieto, “librosa: Audio and music signal analysis in python,” in
2015
Cited alongside, same era.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot
2016
Cited alongside, same era.
K. Arulkumaran, M. P. Deisenroth, M. Brundage, and A. A. Bharath, “A brief survey of deep reinforcement learning,”
2017
Cited alongside, same era.
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in
2017
S. Calinon, “Learning from demonstration (programming by demonstration),”
2018
Later among the works it cites.
2018
Later among the works it cites.
T. Blau, L. Ott, and F. Ramos, “Improving reinforcement learning pre-training with variational dropout,” in
2018
Later among the works it cites.
S. Latif, M. Usman, R. Rana, and J. Qadir, “Phonocardiographic sensing using deep learning for abnormal heartbeat detection,”
2018
Later among the works it cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,”
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2017
Cited alongside, same era.
O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Küttler, J. Agapiou, J. Schrittwieser
2017
Cited alongside, same era.
V. Kurin, S. Nowozin, K. Hofmann, L. Beyer, and B. Leibe, “The atari grand challenge dataset,”
2017
Cited alongside, same era.
T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband
2018
Cited alongside, same era.
2019
Later among the works it cites.
S. Latif, R. Rana, S. Khalifa, R. Jurdak, and J. Epps, “Direct Modelling of Speech Emotion from Raw Speech,” in
2019
Later among the works it cites.
2020
Closest in time.
S. Latif, R. Rana, S. Khalifa, R. Jurdak, J. Epps, and B. W. Schuller, “Multi-task semi-supervised adversarial autoencoding for speech emotion recognition,”
2020
Closest in time.