暂无分享,去创建一个
Jürgen Schmidhuber | Faustino J. Gomez | Daan Wierstra | Matteo Gagliolo | J. Schmidhuber | Daan Wierstra | F. Gomez | M. Gagliolo
[1] R. Penrose. A Generalized inverse for matrices , 1955 .
[2] Ingo Rechenberg,et al. Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .
[3] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[4] W. Vent,et al. Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .
[5] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .
[6] Editors , 1986, Brain Research Bulletin.
[7] Xin Yao,et al. A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..
[8] Yoshua Bengio,et al. Diffusion of Credit in Markovian Models , 1994, NIPS.
[9] Barak A. Pearlmutter. Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.
[10] Vladimir Vapnik,et al. The Nature of Statistical Learning , 1995 .
[11] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[12] F. Girosi,et al. Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[13] Gunnar Rätsch,et al. Predicting Time Series with Support Vector Machines , 1997, ICANN.
[14] David Haussler,et al. Exploiting Generative Models in Discriminative Classifiers , 1998, NIPS.
[15] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[16] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[17] J. Suykens,et al. Recurrent least squares support vector machines , 2000 .
[18] Nello Cristianini,et al. Classification using String Kernels , 2000 .
[19] Shigeki Sagayama,et al. Dynamic Time-Alignment Kernel in Support Vector Machine , 2001, NIPS.
[20] Michael C. Mozer,et al. A Discrete Probabilistic Memory Model for Discovering Dependencies in Time , 2001, ICANN.
[21] Jürgen Schmidhuber,et al. LSTM recurrent networks learn simple context-free and context-sensitive languages , 2001, IEEE Trans. Neural Networks.
[22] Jürgen Schmidhuber,et al. Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..
[23] Samy Bengio,et al. Torch: a modular machine learning software library , 2002 .
[24] Jürgen Schmidhuber,et al. Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM , 2002, Neural Computation.
[25] Simon King,et al. Framewise phone classification using support vector machines , 2002, INTERSPEECH.
[26] Risto Miikkulainen,et al. Active Guidance for a Finless Rocket Using Neuroevolution , 2003, GECCO.
[27] Risto Miikkulainen,et al. Robust non-linear control through neuroevolution , 2003 .
[28] Jürgen Schmidhuber,et al. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets , 2003, Neural Networks.
[29] Harald Haas,et al. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.
[30] Tony Jebara,et al. Probability Product Kernels , 2004, J. Mach. Learn. Res..
[31] Jürgen Schmidhuber,et al. Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.
[32] A. Ijspeert,et al. Associative Memory Models Based on Coupled Oscillators Semester Project , 2005 .
[33] Jürgen Schmidhuber,et al. Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Prediction , 2005, IJCAI 2005.
[34] Philipp Slusallek,et al. Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.
[35] Jürgen Schmidhuber,et al. Co-evolving recurrent neurons learn deep memory POMDPs , 2005, GECCO '05.
[36] A. Ijspeert,et al. Dynamic hebbian learning in adaptive frequency oscillators , 2006 .