### Long Short-Term Memory

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[1] Pineda. Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[2] PAUL J. WERBOS,et al. Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[3] Fernando J. Pineda,et al. Dynamics and architecture for neural computation , 1988, J. Complex..

[4] Barak A. Pearlmutter. Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.

[5] Jürgen Schmidhuber,et al. A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks , 1989 .

[6] David Zipser,et al. Learning Sequential Structure with the Real-Time Recurrent Learning Algorithm , 1991, Int. J. Neural Syst..

[7] Ronald J. Williams,et al. Gradient-Based Learning Algorithms for Recurrent Networks , 1989 .

[8] James L. McClelland,et al. Finite State Automata and Simple Recurrent Networks , 1989, Neural Computation.

[9] Kevin J. Lang. A time delay neural network architecture for speech recognition , 1989 .

[10] Kenji Doya,et al. Adaptive neural oscillator using continuous-time back-propagation learning , 1989, Neural Networks.

[11] Jing Peng,et al. An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories , 1990, Neural Computation.

[12] Jordan B. Pollack,et al. Language Induction by Phase Transition in Dynamical Recognizers , 1990, NIPS.

[13] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..

[14] L. B. Almeida. A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .

[15] Scott E. Fahlman,et al. The Recurrent Cascade-Correlation Architecture , 1990, NIPS.

[16] José Carlos Príncipe,et al. A Theory for Neural Networks with Time Delays , 1990, NIPS.

[17] Geoffrey E. Hinton,et al. A time-delay neural network architecture for isolated word recognition , 1990, Neural Networks.

[18] Jürgen Schmidhuber. Learning Unambiguous Reduced Sequence Descriptions , 1991, NIPS.

[19] Sepp Hochreiter,et al. Untersuchungen zu dynamischen neuronalen Netzen , 1991 .

[20] Pierre Baldi,et al. Contrastive Learning and Neural Oscillations , 1991, Neural Computation.

[21] Michael C. Mozer,et al. Induction of Multiscale Temporal Structure , 1991, NIPS.

[22] Jürgen Schmidhuber,et al. Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.

[23] Jürgen Schmidhuber,et al. A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks , 1992, Neural Computation.

[24] Raymond L. Watrous,et al. Induction of Finite-State Languages Using Second-Order Recurrent Networks , 1992, Neural Computation.

[25] Guo-Zheng Sun,et al. Time Warping Invariant Neural Networks , 1992, NIPS.

[26] Mark B. Ring. Learning Sequential Tasks by Incrementally Adding Higher Orders , 1992, NIPS.

[27] Tony Plate,et al. Holographic Recurrent Networks , 1992, NIPS.

[28] K. Doya,et al. Bifurcations in the learning of recurrent neural networks , 1992, [Proceedings] 1992 IEEE International Symposium on Circuits and Systems.

[29] Yoshua Bengio,et al. Credit Assignment through Time: Alternatives to Backpropagation , 1993, NIPS.

[30] C. Lee Giles,et al. Experimental Comparison of the Effect of Order in Recurrent Neural Networks , 1993, Int. J. Pattern Recognit. Artif. Intell..

[31] Jürgen Schmidhuber,et al. Continuous history compression , 1993 .

[32] Jürgen Schmidhuber,et al. Netzwerkarchitekturen, Zielfunktionen und Kettenregel , 1993 .

[33] Lee A. Feldkamp,et al. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[34] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[35] Peter Tiňo,et al. Learning long-term dependencies is not as difficult with NARX recurrent neural networks , 1995 .

[36] Ronald J. Williams,et al. Gradient-based learning algorithms for recurrent networks and their computational complexity , 1995 .

[37] Barak A. Pearlmutter. Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[38] Corso Elvezia. Bridging Long Time Lags by Weight Guessing and \long Short Term Memory" , 1996 .

[39] Sepp Hochreiter,et al. Guessing can Outperform Many Long Time Lag Algorithms , 1996 .

[40] Jürgen Schmidhuber,et al. LSTM can Solve Hard Long Time Lag Problems , 1996, NIPS.

[41] Peter Tiño,et al. Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.