메뉴 건너뛰기




Volumn 9, Issue 8, 1997, Pages 1735-1780

Long Short-Term Memory

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; LEARNING; MEMORY; NERVE CELL NETWORK; PHYSIOLOGY; PSYCHOLOGICAL MODEL; SHORT TERM MEMORY; TIME;

EID: 0031573117     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.8.1735     Document Type: Article
Times cited : (86893)

References (42)
  • 1
    • 0023563286 scopus 로고
    • A learning rule for asynchronous perceptrons with feedback in a combinatorial environment
    • Almeida, L. B. (1987). A learning rule for asynchronous perceptrons with feedback in a combinatorial environment. In IEEE 1st International Conference on Neural Networks, San Diego (Vol. 2, pp. 609-618).
    • (1987) IEEE 1st International Conference on Neural Networks, San Diego , vol.2 , pp. 609-618
    • Almeida, L.B.1
  • 2
    • 0043170587 scopus 로고
    • Contrastive learning and neural oscillator
    • Baldi, P., & Pineda, F. (1991). Contrastive learning and neural oscillator. Neural Computation, 3, 526-545.
    • (1991) Neural Computation , vol.3 , pp. 526-545
    • Baldi, P.1    Pineda, F.2
  • 3
    • 0000971250 scopus 로고
    • Credit assignment through time: Alternatives to backpropagation
    • J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), San Mateo, CA: Morgan Kaufmann
    • Bengio, Y., & Frasconi, P. (1994). Credit assignment through time: Alternatives to backpropagation. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems 6 (pp. 75-82). San Mateo, CA: Morgan Kaufmann.
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 75-82
    • Bengio, Y.1    Frasconi, P.2
  • 4
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157-166.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.2 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 6
    • 0343449995 scopus 로고
    • A theory for neural networks with time delays
    • R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), San Mateo, CA: Morgan Kaufmann
    • de Vries, B., & Principe, J. C. (1991). A theory for neural networks with time delays. In R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), Advances in neural information processing systems 3, (pp. 162-168). San Mateo, CA: Morgan Kaufmann.
    • (1991) Advances in Neural Information Processing Systems , vol.3 , pp. 162-168
    • De Vries, B.1    Principe, J.C.2
  • 8
    • 0024875962 scopus 로고
    • Adaptive neural oscillator using continuous-time backpropagation learning
    • Doya, K., & Yoshizawa, S. (1989). Adaptive neural oscillator using continuous-time backpropagation learning. Neural Networks, 2, 375-385.
    • (1989) Neural Networks , vol.2 , pp. 375-385
    • Doya, K.1    Yoshizawa, S.2
  • 9
    • 0004262806 scopus 로고
    • Tech. Rep. No. CRL 8801. San Diego: Center for Research in Language, University of California, San Diego
    • Elman, J. L. (1988). Finding structure in time (Tech. Rep. No. CRL 8801). San Diego: Center for Research in Language, University of California, San Diego.
    • (1988) Finding Structure in Time
    • Elman, J.L.1
  • 10
    • 0001086881 scopus 로고
    • The recurrent cascade-correlation learning algorithm
    • R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), San Mateo, CA: Morgan Kaufmann
    • Fahlman, S. E. (1991). The recurrent cascade-correlation learning algorithm. In R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), Advances in neural information processing systems 3 (pp. 190-196). San Mateo, CA: Morgan Kaufmann.
    • (1991) Advances in Neural Information Processing Systems , vol.3 , pp. 190-196
    • Fahlman, S.E.1
  • 11
    • 0003575034 scopus 로고
    • Diploma thesis, Institut für Informatik, Lehrstuhl Prof. Brauer, Technische Universität München.
    • Hochreiter, J. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, Institut für Informatik, Lehrstuhl Prof. Brauer, Technische Universität München. See http://www7.informatik.tu-muenchen.de/∼hochreit.
    • (1991) Untersuchungen zu Dynamischen Neuronalen Netzen
    • Hochreiter, J.1
  • 12
    • 84886287164 scopus 로고
    • Tech. Rep. No. FKI-207-95. Fakultät für Informatik, Technische Universität München
    • Hochreiter, S., & Schmidhuber, J. (1995). Long short-term memory (Tech. Rep. No. FKI-207-95). Fakultät für Informatik, Technische Universität München.
    • (1995) Long Short-term Memory
    • Hochreiter, S.1    Schmidhuber, J.2
  • 13
    • 0346176694 scopus 로고    scopus 로고
    • Bridging long time lags by weight guessing and "long short-term memory."
    • F. L. Silva, J. C. Principe, & L. B. Almeida (Eds.), Amsterdam: IOS Press
    • Hochreiter, S., & Schmidhuber, J. (1996). Bridging long time lags by weight guessing and "long short-term memory." In F. L. Silva, J. C. Principe, & L. B. Almeida (Eds.), Spatiotemporal models in biological and artificial systems (pp. 65-72). Amsterdam: IOS Press.
    • (1996) Spatiotemporal Models in Biological and Artificial Systems , pp. 65-72
    • Hochreiter, S.1    Schmidhuber, J.2
  • 15
    • 0025254722 scopus 로고
    • A time-delay neural network architecture for isolated word recognition
    • Lang, K., Waibel, A., & Hinton, G. E. (1990). A time-delay neural network architecture for isolated word recognition. Neural Networks, 3, 23-43.
    • (1990) Neural Networks , vol.3 , pp. 23-43
    • Lang, K.1    Waibel, A.2    Hinton, G.E.3
  • 18
    • 0008554931 scopus 로고
    • A focused back-propagation algorithm for temporal sequence recognition
    • Mozer, M. C. (1989). A focused back-propagation algorithm for temporal sequence recognition. Complex Systems, 3, 349-381.
    • (1989) Complex Systems , vol.3 , pp. 349-381
    • Mozer, M.C.1
  • 19
    • 0005316958 scopus 로고
    • Induction of multiscale temporal structure
    • J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), San Mateo, CA: Morgan Kaufmann
    • Mozer, M. C. (1992). Induction of multiscale temporal structure. In J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), Advances in neural information processing systems 4 (pp. 275-282). San Mateo, CA: Morgan Kaufmann.
    • (1992) Advances in Neural Information Processing Systems , vol.4 , pp. 275-282
    • Mozer, M.C.1
  • 20
    • 0001202597 scopus 로고
    • Learning state space trajectories in recurrent neural networks
    • Pearlmutter, B. A. (1989). Learning state space trajectories in recurrent neural networks. Neural Computation, 1(2), 263-269.
    • (1989) Neural Computation , vol.1 , Issue.2 , pp. 263-269
    • Pearlmutter, B.A.1
  • 21
    • 0029375851 scopus 로고
    • Gradient calculations for dynamic recurrent neural networks: A survey
    • Pearlmutter, B. A. (1995). Gradient calculations for dynamic recurrent neural networks: A survey. IEEE Transactions on Neural Networks, 6(5), 1212-1228.
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.5 , pp. 1212-1228
    • Pearlmutter, B.A.1
  • 22
    • 0000442791 scopus 로고
    • Generalization of back-propagation to recurrent neural networks
    • Pineda, F. J. (1987). Generalization of back-propagation to recurrent neural networks. Physical Review Letters, 19(59), 2229-2232.
    • (1987) Physical Review Letters , vol.19 , Issue.59 , pp. 2229-2232
    • Pineda, F.J.1
  • 23
    • 0000599735 scopus 로고
    • Dynamics and architecture for neural computation
    • Pineda, F. J. (1988). Dynamics and architecture for neural computation. Journal of Complexity, 4, 216-245.
    • (1988) Journal of Complexity , vol.4 , pp. 216-245
    • Pineda, F.J.1
  • 24
    • 0009382953 scopus 로고
    • Holographic recurrent networks
    • S. J. Hanson, J. D. Cowan, & C. L. Giles ( Eds.), San Mateo, CA: Morgan Kaufmann
    • Plate, T. A. (1993). Holographic recurrent networks. In S. J. Hanson, J. D. Cowan, & C. L. Giles ( Eds.), Advances in neural information processing systems 5 (pp. 34-41). San Mateo, CA: Morgan Kaufmann.
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 34-41
    • Plate, T.A.1
  • 25
    • 0346807270 scopus 로고
    • Language induction by phase transition in dynamical recognizers
    • R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), San Mateo, CA: Morgan Kaufmann
    • Pollack, J. B. (1991). Language induction by phase transition in dynamical recognizers. In R. P. Lippmann, J. E. Moody, & D. S. Touretzky (Eds.), Advances in neural information processing systems 3 (pp. 619-626). San Mateo, CA: Morgan Kaufmann.
    • (1991) Advances in Neural Information Processing Systems , vol.3 , pp. 619-626
    • Pollack, J.B.1
  • 26
    • 0028401031 scopus 로고
    • Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
    • Puskorius, G. V., and Feldkamp, L. A. (1994). Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks. IEEE Transactions on Neural Networks, 5(2), 279-297.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.2 , pp. 279-297
    • Puskorius, G.V.1    Feldkamp, L.A.2
  • 27
    • 0007912190 scopus 로고
    • Learning sequential tasks by incrementally adding higher orders
    • S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), San Mateo, CA: Morgan Kaufmann
    • Ring, M. B. (1993). Learning sequential tasks by incrementally adding higher orders. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems 5 (pp. 115-122). San Mateo, CA: Morgan Kaufmann.
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 115-122
    • Ring, M.B.1
  • 29
    • 0001623105 scopus 로고
    • A local learning algorithm for dynamic feedforward and recurrent networks
    • Schmidhuber, J. (1989). A local learning algorithm for dynamic feedforward and recurrent networks. Connection Science, 1(4), 403-412.
    • (1989) Connection Science , vol.1 , Issue.4 , pp. 403-412
    • Schmidhuber, J.1
  • 30
    • 0000053463 scopus 로고
    • 3) time complexity learning algorithm for fully recurrent continually running networks
    • 3) time complexity learning algorithm for fully recurrent continually running networks. Neural Computation, 4(2), 243-248.
    • (1992) Neural Computation , vol.4 , Issue.2 , pp. 243-248
    • Schmidhuber, J.1
  • 31
    • 0001033889 scopus 로고
    • Learning complex, extended sequences using the principle of history compression
    • Schmidhuber, J. (1992b). Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2), 234-242.
    • (1992) Neural Computation , vol.4 , Issue.2 , pp. 234-242
    • Schmidhuber, J.1
  • 32
    • 0348068168 scopus 로고
    • Learning unambiguous reduced sequence descriptions
    • J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), San Mateo, CA: Morgan Kaufmann
    • Schmidhuber, J. (1992c). Learning unambiguous reduced sequence descriptions. In J. E. Moody, S. J. Hanson, & R. P. Lippman (Eds.), Advances in neural information processing systems 4 (pp. 291-298). San Mateo, CA: Morgan Kaufmann.
    • (1992) Advances in Neural Information Processing Systems , vol.4 , pp. 291-298
    • Schmidhuber, J.1
  • 34
    • 0043063195 scopus 로고    scopus 로고
    • Tech. Rep. No. IDSIA-19-96. Lugano, Switzerland: Instituto Dalle Molle di Studi sull'Intelligenza Artificiale
    • Schmidhuber, J., & Hochreiter, S. (1996). Guessing can outperform many long time lag algorithms (Tech. Rep. No. IDSIA-19-96). Lugano, Switzerland: Instituto Dalle Molle di Studi sull'Intelligenza Artificiale.
    • (1996) Guessing Can Outperform Many Long Time Lag Algorithms
    • Schmidhuber, J.1    Hochreiter, S.2
  • 36
    • 0001274675 scopus 로고
    • Learning sequential structures with the real-time recurrent learning algorithm
    • Smith, A. W., & Zipser, D. (1989). Learning sequential structures with the real-time recurrent learning algorithm. International Journal of Neural Systems, 1(2), 125-131.
    • (1989) International Journal of Neural Systems , vol.1 , Issue.2 , pp. 125-131
    • Smith, A.W.1    Zipser, D.2
  • 37
    • 0000651310 scopus 로고
    • Time warping invariant neural networks
    • S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), San Mateo, CA: Morgan Kaufmann
    • Sun, G., Chen, H., & Lee, Y. (1993). Time warping invariant neural networks. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems 5 (pp. 180-187). San Mateo, CA: Morgan Kaufmann.
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 180-187
    • Sun, G.1    Chen, H.2    Lee, Y.3
  • 38
    • 0001601299 scopus 로고
    • Induction of finite-state languages using second-order recurrent networks
    • Watrous, R. L., & Kuhn, G. M. (1992). Induction of finite-state languages using second-order recurrent networks. Neural Computation, 4, 406-414.
    • (1992) Neural Computation , vol.4 , pp. 406-414
    • Watrous, R.L.1    Kuhn, G.M.2
  • 39
    • 0000903748 scopus 로고
    • Generalization of backpropagation with application to a recurrent gas market model
    • Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1, 339-356.
    • (1988) Neural Networks , vol.1 , pp. 339-356
    • Werbos, P.J.1
  • 41
    • 0001609567 scopus 로고
    • An efficient gradient-based algorithm for on-line training of recurrent network trajectories
    • Williams, R. J. & Peng, J. (1990). An efficient gradient-based algorithm for on-line training of recurrent network trajectories. Neural Computation, 4, 491-501.
    • (1990) Neural Computation , vol.4 , pp. 491-501
    • Williams, R.J.1    Peng, J.2
  • 42
    • 0001765578 scopus 로고
    • Gradient-based learning algorithms for recurrent networks and their computational complexity
    • Y. Chauvin, & D. E. Rumelhart (Eds.), Hillsdale, NJ: Erlbaum
    • Williams, R. J., & Zipser, D. (1992). Gradient-based learning algorithms for recurrent networks and their computational complexity. In Y. Chauvin, & D. E. Rumelhart (Eds.), Back-propagation: Theory, architectures and applications. Hillsdale, NJ: Erlbaum.
    • (1992) Back-propagation: Theory, Architectures and Applications
    • Williams, R.J.1    Zipser, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.