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Jürgen Schmidhuber: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Jürgen Schmidhuber
    Gödel Machines: Towards a Technical Justification of Consciousness. [Citation Graph (0, 0)][DBLP]
    Adaptive Agents and Multi-Agent Systems, 2005, pp:1-23 [Conf]
  2. Alex Graves, Douglas Eck, Nicole Beringer, Jürgen Schmidhuber
    Biologically Plausible Speech Recognition with LSTM Neural Nets. [Citation Graph (0, 0)][DBLP]
    BioADIT, 2004, pp:127-136 [Conf]
  3. Jürgen Schmidhuber
    A Computer Scientist's View of Life, the Universe, and Everything. [Citation Graph (0, 0)][DBLP]
    Foundations of Computer Science: Potential - Theory - Cognition, 1997, pp:201-208 [Conf]
  4. Alexey V. Chernov, Jürgen Schmidhuber
    Prefix-Like Complexities and Computability in the Limit. [Citation Graph (0, 0)][DBLP]
    CiE, 2006, pp:85-93 [Conf]
  5. Jürgen Schmidhuber
    The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions. [Citation Graph (0, 0)][DBLP]
    COLT, 2002, pp:216-228 [Conf]
  6. Matteo Gagliolo, Jürgen Schmidhuber
    Impact of Censored Sampling on the Performance of Restart Strategies. [Citation Graph (0, 0)][DBLP]
    CP, 2006, pp:167-181 [Conf]
  7. Jürgen Schmidhuber, Jieyu Zhao
    Multi-Agent Learning with the Success-Story Algorithm. [Citation Graph (0, 0)][DBLP]
    ECAI Workshop LDAIS / ICMAS Workshop LIOME, 1996, pp:82-93 [Conf]
  8. Matteo Gagliolo, Viktor Zhumatiy, Jürgen Schmidhuber
    Adaptive Online Time Allocation to Search Algorithms. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:134-143 [Conf]
  9. Faustino J. Gomez, Jürgen Schmidhuber, Risto Miikkulainen
    Efficient Non-linear Control Through Neuroevolution. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:654-662 [Conf]
  10. Rafal Salustowicz, Jürgen Schmidhuber
    Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space. [Citation Graph (0, 0)][DBLP]
    ECML, 1997, pp:213-220 [Conf]
  11. Marco Wiering, Jürgen Schmidhuber
    Speeding up Q(lambda)-Learning. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:352-363 [Conf]
  12. Felix A. Gers, Juan Antonio Pérez-Ortiz, Douglas Eck, Jürgen Schmidhuber
    DEKF-LSTM. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:369-376 [Conf]
  13. Jürgen Schmidhuber, Matteo Gagliolo, Daan Wierstra, Faustino J. Gomez
    Evolino for recurrent support vector machines. [Citation Graph (0, 0)][DBLP]
    ESANN, 2006, pp:593-598 [Conf]
  14. Faustino J. Gomez, Jürgen Schmidhuber
    Co-evolving recurrent neurons learn deep memory POMDPs. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:491-498 [Conf]
  15. Daan Wierstra, Faustino J. Gomez, Jürgen Schmidhuber
    Modeling systems with internal state using evolino. [Citation Graph (0, 0)][DBLP]
    GECCO, 2005, pp:1795-1802 [Conf]
  16. Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter, Jürgen Schmidhuber
    Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot. [Citation Graph (0, 0)][DBLP]
    IAS, 2006, pp:272-281 [Conf]
  17. Nicole Beringer, Alex Graves, Florian Schiel, Jürgen Schmidhuber
    Classifying Unprompted Speech by Retraining LSTM Nets. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2005, pp:575-581 [Conf]
  18. Douglas Eck, Jürgen Schmidhuber
    Learning the Long-Term Structure of the Blues. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:284-289 [Conf]
  19. Matteo Gagliolo, Jürgen Schmidhuber
    A Neural Network Model for Inter-problem Adaptive Online Time Allocation. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:7-12 [Conf]
  20. Felix A. Gers, Douglas Eck, Jürgen Schmidhuber
    Applying LSTM to Time Series Predictable through Time-Window Approaches. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:669-676 [Conf]
  21. Felix A. Gers, Juan Antonio Pérez-Ortiz, Douglas Eck, Jürgen Schmidhuber
    Learning Context Sensitive Languages with LSTM Trained with Kalman Filters. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:655-660 [Conf]
  22. Martijn van de Giessen, Jürgen Schmidhuber
    Fast Color-Based Object Recognition Independent of Position and Orientation. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2005, pp:469-474 [Conf]
  23. Faustino J. Gomez, Jürgen Schmidhuber
    Evolving Modular Fast-Weight Networks for Control. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:383-389 [Conf]
  24. Alex Graves, Santiago Fernández, Jürgen Schmidhuber
    Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:799-804 [Conf]
  25. Sepp Hochreiter, Jürgen Schmidhuber
    Unsupervised Coding with LOCOCODE. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:655-660 [Conf]
  26. Magdalena Klapper-Rybicka, Nicol N. Schraudolph, Jürgen Schmidhuber
    Unsupervised Learning in LSTM Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:684-691 [Conf]
  27. Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
    Market-Based Reinforcement Learning in Partially Observable Worlds. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:865-873 [Conf]
  28. Michele Milano, Jürgen Schmidhuber, Petros Koumoutsakos
    Active Learning with Adaptive Grids. [Citation Graph (0, 0)][DBLP]
    ICANN, 2001, pp:436-442 [Conf]
  29. Juan Antonio Pérez-Ortiz, Jürgen Schmidhuber, Felix A. Gers, Douglas Eck
    Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:1055-1069 [Conf]
  30. Rafal Salustowicz, Marco Wiering, Jürgen Schmidhuber
    On Learning Soccer Strategies. [Citation Graph (0, 0)][DBLP]
    ICANN, 1997, pp:769-774 [Conf]
  31. Jürgen Schmidhuber
    Completely Self-referential Optimal Reinforcement Learners. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2005, pp:223-233 [Conf]
  32. Alex Graves, Santiago Fernández, Faustino J. Gomez, Jürgen Schmidhuber
    Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:369-376 [Conf]
  33. Rafal Salustowicz, Jürgen Schmidhuber
    Evolving Structured Programs with Hierarchical Instructions and Skip Nodes. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:488-496 [Conf]
  34. Jürgen Schmidhuber
    Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability. [Citation Graph (0, 0)][DBLP]
    ICML, 1995, pp:488-496 [Conf]
  35. Marco Wiering, Jürgen Schmidhuber
    Solving POMDPs with Levin Search and EIRA. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:534-542 [Conf]
  36. Jürgen Schmidhuber, Daan Wierstra, Faustino J. Gomez
    Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2005, pp:853-858 [Conf]
  37. Santiago Fernández, Alex Graves, Jürgen Schmidhuber
    Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:774-779 [Conf]
  38. Matteo Gagliolo, Jürgen Schmidhuber
    Learning Restart Strategies. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:792-797 [Conf]
  39. Felix A. Gers, Jürgen Schmidhuber
    Recurrent Nets that Time and Count. [Citation Graph (0, 0)][DBLP]
    IJCNN (3), 2000, pp:189-194 [Conf]
  40. Felix A. Gers, Jürgen Schmidhuber
    Neural Processing of Complex Continual Input Streams. [Citation Graph (0, 0)][DBLP]
    IJCNN (4), 2000, pp:557-562 [Conf]
  41. Sepp Hochreiter, Jürgen Schmidhuber
    Nonlinear ICA through low-complexity autoencoders. [Citation Graph (0, 0)][DBLP]
    ISCAS (5), 1999, pp:53-56 [Conf]
  42. Bram Bakker, Jürgen Schmidhuber
    Hierarchical reinforcement learning with subpolicies specializing for learned subgoals. [Citation Graph (0, 0)][DBLP]
    Neural Networks and Computational Intelligence, 2004, pp:125-130 [Conf]
  43. Alex Graves, Nicole Beringer, Jürgen Schmidhuber
    A comparison between spiking and differentiable recurrent neural networks on spoken digit recognition. [Citation Graph (0, 0)][DBLP]
    Neural Networks and Computational Intelligence, 2004, pp:164-168 [Conf]
  44. Sepp Hochreiter, Jürgen Schmidhuber
    Simplifying Neural Nets by Discovering Flat Minima. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:529-536 [Conf]
  45. Sepp Hochreiter, Jürgen Schmidhuber
    LSTM can Solve Hard Long Time Lag Problems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:473-479 [Conf]
  46. Sepp Hochreiter, Jürgen Schmidhuber
    Source Separation as a By-Product of Regularization. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:459-465 [Conf]
  47. Jürgen Schmidhuber
    Bias-Optimal Incremental Problem Solving. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1547-1546 [Conf]
  48. Jürgen Schmidhuber
    Reinforcement Learning in Markovian and Non-Markovian Environments. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:500-506 [Conf]
  49. Jürgen Schmidhuber
    Learning Unambiguous Reduced Sequence Descriptions. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:291-298 [Conf]
  50. Jürgen Schmidhuber, Stefan Heil
    Predictive Coding with Neural Nets: Application to Text Compression. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:1047-1054 [Conf]
  51. Jürgen Schmidhuber
    Sequential Decision Making Based on Direct Search. [Citation Graph (0, 0)][DBLP]
    Sequence Learning, 2001, pp:213-240 [Conf]
  52. Marco Wiering, Rafal Salustowicz, Jürgen Schmidhuber
    Reinforcement Learning Soccer Teams with Incomplete World Models. [Citation Graph (0, 0)][DBLP]
    Auton. Robots, 1999, v:7, n:1, pp:77-88 [Journal]
  53. Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
    Market-Based Reinforcement Learning in Partially Observable Worlds [Citation Graph (0, 0)][DBLP]
    CoRR, 2001, v:0, n:, pp:- [Journal]
  54. Ivo Kwee, Marcus Hutter, Jürgen Schmidhuber
    Gradient-based Reinforcement Planning in Policy-Search Methods [Citation Graph (0, 0)][DBLP]
    CoRR, 2001, v:0, n:, pp:- [Journal]
  55. Jürgen Schmidhuber
    Optimal Ordered Problem Solver [Citation Graph (0, 0)][DBLP]
    CoRR, 2002, v:0, n:, pp:- [Journal]
  56. Jürgen Schmidhuber
    The New AI: General & Sound & Relevant for Physics [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  57. Jürgen Schmidhuber
    Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  58. Jürgen Schmidhuber
    Algorithmic Theories of Everything [Citation Graph (0, 0)][DBLP]
    CoRR, 2000, v:0, n:, pp:- [Journal]
  59. Jürgen Schmidhuber
    A Computer Scientist's View of Life, the Universe, and Everything [Citation Graph (0, 0)][DBLP]
    CoRR, 1999, v:0, n:, pp:- [Journal]
  60. Rafal Salustowicz, Jürgen Schmidhuber
    Probabilistic Incremental Program Evolution. [Citation Graph (0, 0)][DBLP]
    Evolutionary Computation, 1997, v:5, n:2, pp:123-141 [Journal]
  61. Alexey V. Chernov, Marcus Hutter, Jürgen Schmidhuber
    Algorithmic complexity bounds on future prediction errors. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 2007, v:205, n:2, pp:242-261 [Journal]
  62. Jürgen Schmidhuber
    Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit. [Citation Graph (0, 0)][DBLP]
    Int. J. Found. Comput. Sci., 2002, v:13, n:4, pp:587-612 [Journal]
  63. Jürgen Schmidhuber, Rudolf Huber
    Learning to Generate Artificial Fovea Trajectories for Target Detection. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 1991, v:2, n:1-2, pp:125-134 [Journal]
  64. Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber
    Learning Precise Timing with LSTM Recurrent Networks. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:115-143 [Journal]
  65. Rafal Salustowicz, Marco Wiering, Jürgen Schmidhuber
    Learning Team Strategies: Soccer Case Studies. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:33, n:2-3, pp:263-282 [Journal]
  66. Jürgen Schmidhuber
    Optimal Ordered Problem Solver. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2004, v:54, n:3, pp:211-254 [Journal]
  67. Jürgen Schmidhuber, Jieyu Zhao, Marco Wiering
    Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:28, n:1, pp:105-130 [Journal]
  68. Marco Wiering, Jürgen Schmidhuber
    Fast Online Q(lambda). [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:33, n:1, pp:105-115 [Journal]
  69. Felix A. Gers, Jürgen Schmidhuber, Fred A. Cummins
    Learning to Forget: Continual Prediction with LSTM. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:10, pp:2451-2471 [Journal]
  70. Sepp Hochreiter, Jürgen Schmidhuber
    Flat Minima [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:1, pp:1-42 [Journal]
  71. Sepp Hochreiter, Jürgen Schmidhuber
    Feature Extraction Through LOCOCODE. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1999, v:11, n:3, pp:679-714 [Journal]
  72. Sepp Hochreiter, Jürgen Schmidhuber
    Long Short-Term Memory. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:8, pp:1735-1780 [Journal]
  73. Jürgen Schmidhuber, Felix A. Gers, Douglas Eck
    Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:9, pp:2039-2041 [Journal]
  74. Jürgen Schmidhuber, Daan Wierstra, Matteo Gagliolo, Faustino J. Gomez
    Training Recurrent Networks by Evolino. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2007, v:19, n:3, pp:757-779 [Journal]
  75. Alex Graves, Jürgen Schmidhuber
    Framewise phoneme classification with bidirectional LSTM and other neural network architectures. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:602-610 [Journal]
  76. Juan Antonio Pérez-Ortiz, Felix A. Gers, Douglas Eck, Jürgen Schmidhuber
    Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2003, v:16, n:2, pp:241-250 [Journal]
  77. Jürgen Schmidhuber
    Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1997, v:10, n:5, pp:857-873 [Journal]
  78. Jürgen Schmidhuber
    Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity. [Citation Graph (0, 0)][DBLP]
    ALT, 2007, pp:32-33 [Conf]
  79. Jürgen Schmidhuber
    Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 2007, pp:26-38 [Conf]
  80. Daan Wierstra, Jürgen Schmidhuber
    Policy Gradient Critics. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:466-477 [Conf]
  81. Alex Graves, Santiago Fernández, Jürgen Schmidhuber
    Multi-dimensional Recurrent Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2007, pp:549-558 [Conf]
  82. Santiago Fernández, Alex Graves, Jürgen Schmidhuber
    An Application of Recurrent Neural Networks to Discriminative Keyword Spotting. [Citation Graph (0, 0)][DBLP]
    ICANN (2), 2007, pp:220-229 [Conf]
  83. Daan Wierstra, Alexander Förster, Jan Peters, Jürgen Schmidhuber
    Solving Deep Memory POMDPs with Recurrent Policy Gradients. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2007, pp:697-706 [Conf]
  84. Matteo Gagliolo, Jürgen Schmidhuber
    Learning dynamic algorithm portfolios. [Citation Graph (0, 0)][DBLP]
    Ann. Math. Artif. Intell., 2006, v:47, n:3-4, pp:295-328 [Journal]
  85. Alexey V. Chernov, Marcus Hutter, Jürgen Schmidhuber
    Algorithmic Complexity Bounds on Future Prediction Errors [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  86. Alex Graves, Santiago Fernández, Jürgen Schmidhuber
    Multi-Dimensional Recurrent Neural Networks [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  87. Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter, Jürgen Schmidhuber
    Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  88. Jürgen Schmidhuber
    New Millennium AI and the Convergence of History [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  89. Jürgen Schmidhuber
    2006: Celebrating 75 years of AI - History and Outlook: the Next 25 Years [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  90. Daniil Ryabko, Jürgen Schmidhuber
    Using Data Compressors to Construct Rank Tests [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  91. Jürgen Schmidhuber
    Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]

  92. Complexity Monotone in Conditions and Future Prediction Errors. [Citation Graph (, )][DBLP]


  93. RNN-based Learning of Compact Maps for Efficient Robot Localization. [Citation Graph (, )][DBLP]


  94. Efficient natural evolution strategies. [Citation Graph (, )][DBLP]


  95. Evolving neural networks in compressed weight space. [Citation Graph (, )][DBLP]


  96. Exponential natural evolution strategies. [Citation Graph (, )][DBLP]


  97. Policy Gradients with Parameter-Based Exploration for Control. [Citation Graph (, )][DBLP]


  98. Episodic Reinforcement Learning by Logistic Reward-Weighted Regression. [Citation Graph (, )][DBLP]


  99. An EM Based Training Algorithm for Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  100. Scalable Neural Networks for Board Games. [Citation Graph (, )][DBLP]


  101. Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning. [Citation Graph (, )][DBLP]


  102. Evolving Memory Cell Structures for Sequence Learning. [Citation Graph (, )][DBLP]


  103. Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients. [Citation Graph (, )][DBLP]


  104. Policy Gradients for Cryptanalysis. [Citation Graph (, )][DBLP]


  105. Stochastic search using the natural gradient. [Citation Graph (, )][DBLP]


  106. Quasi-online Reinforcement Learning for Robots. [Citation Graph (, )][DBLP]


  107. Driven by Compression Progress. [Citation Graph (, )][DBLP]


  108. Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  109. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  110. State-Dependent Exploration for Policy Gradient Methods. [Citation Graph (, )][DBLP]


  111. Formal Theory of Fun and Creativity. [Citation Graph (, )][DBLP]


  112. Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. [Citation Graph (, )][DBLP]


  113. Fitness Expectation Maximization. [Citation Graph (, )][DBLP]


  114. Countering Poisonous Inputs with Memetic Neuroevolution. [Citation Graph (, )][DBLP]


  115. A Natural Evolution Strategy for Multi-objective Optimization. [Citation Graph (, )][DBLP]


  116. Learning what to ignore: Memetic climbing in topology and weight space. [Citation Graph (, )][DBLP]


  117. Natural Evolution Strategies. [Citation Graph (, )][DBLP]


  118. Robust player imitation using multiobjective evolution. [Citation Graph (, )][DBLP]


  119. 2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years. [Citation Graph (, )][DBLP]


  120. A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  121. Towards Distributed Algorithm Portfolios. [Citation Graph (, )][DBLP]


  122. Algorithm Selection as a Bandit Problem with Unbounded Losses. [Citation Graph (, )][DBLP]


  123. Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes. [Citation Graph (, )][DBLP]


  124. Using data compressors to construct order tests for homogeneity and component independence. [Citation Graph (, )][DBLP]


  125. A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. [Citation Graph (, )][DBLP]


  126. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. [Citation Graph (, )][DBLP]


  127. Evolino for recurrent support vector machines [Citation Graph (, )][DBLP]


  128. Phoneme recognition in TIMIT with BLSTM-CTC [Citation Graph (, )][DBLP]


  129. Algorithm Selection as a Bandit Problem with Unbounded Losses [Citation Graph (, )][DBLP]


  130. Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes [Citation Graph (, )][DBLP]


  131. Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition [Citation Graph (, )][DBLP]


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