Year |
Citation |
Score |
2023 |
Gopalakrishnan A, Irie K, Schmidhuber J, van Steenkiste S. Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers. Neural Computation. 35: 593-626. PMID 36746145 DOI: 10.1162/neco_a_01567 |
0.341 |
|
2021 |
Rauber P, Ummadisingu A, Mutz F, Schmidhuber J. Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients. Neural Computation. 33: 1498-1553. PMID 34496391 DOI: 10.1162/neco_a_01387 |
0.309 |
|
2020 |
van Steenkiste S, Kurach K, Schmidhuber J, Gelly S. Investigating object compositionality in Generative Adversarial Networks. Neural Networks : the Official Journal of the International Neural Network Society. 130: 309-325. PMID 32736226 DOI: 10.1016/J.Neunet.2020.07.007 |
0.388 |
|
2020 |
Schmidhuber J. Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991). Neural Networks : the Official Journal of the International Neural Network Society. 127: 58-66. PMID 32334341 DOI: 10.1016/J.Neunet.2020.04.008 |
0.371 |
|
2016 |
Greff K, Srivastava RK, Koutnik J, Steunebrink BR, Schmidhuber J. LSTM: A Search Space Odyssey. Ieee Transactions On Neural Networks and Learning Systems. PMID 27411231 DOI: 10.1109/Tnnls.2016.2582924 |
0.397 |
|
2016 |
Kompella VR, Luciw M, Stollenga MF, Schmidhuber J. Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis. Neural Computation. 1-64. PMID 27348735 DOI: 10.1162/Neco_A_00855 |
0.446 |
|
2015 |
Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, Schmidhuber J, Laptev D, Dwivedi S, Buhmann JM, Liu T, Seyedhosseini M, Tasdizen T, Kamentsky L, Burget R, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in Neuroanatomy. 9: 142. PMID 26594156 DOI: 10.3389/Fnana.2015.00142 |
0.311 |
|
2015 |
Schmidhuber J. Deep learning in neural networks: an overview. Neural Networks : the Official Journal of the International Neural Network Society. 61: 85-117. PMID 25462637 DOI: 10.1016/J.Neunet.2014.09.003 |
0.437 |
|
2015 |
Kompella VR, Stollenga M, Luciw M, Schmidhuber J. Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots Artificial Intelligence. DOI: 10.1016/J.Artint.2015.02.001 |
0.441 |
|
2014 |
Masci J, Bronstein MM, Bronstein AM, Schmidhuber J. Multimodal Similarity-Preserving Hashing. Ieee Transactions On Pattern Analysis and Machine Intelligence. 36: 824-30. PMID 26353203 DOI: 10.1109/Tpami.2013.225 |
0.452 |
|
2014 |
Frank M, Leitner J, Stollenga M, Förster A, Schmidhuber J. Curiosity driven reinforcement learning for motion planning on humanoids. Frontiers in Neurorobotics. 7: 25. PMID 24432001 DOI: 10.3389/Fnbot.2013.00025 |
0.721 |
|
2014 |
Ngo H, Luciw M, Nagi J, Forster A, Schmidhuber J, Vien NA. Efficient Interactive Multiclass Learning from Binary Feedback Acm Transactions On Interactive Intelligent Systems. 4: 1-25. DOI: 10.1145/2629631 |
0.44 |
|
2014 |
Luciw M, Kazerounian S, Sandamirskaya Y, Schöner G, Schmidhuber J. Reinforcement-driven shaping of sequence learning in neural dynamics Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8575: 198-209. DOI: 10.1007/978-3-319-08864-8_19 |
0.327 |
|
2013 |
Ngo H, Luciw M, Förster A, Schmidhuber J. Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots. Frontiers in Psychology. 4: 833. PMID 24324448 DOI: 10.3389/Fpsyg.2013.00833 |
0.405 |
|
2013 |
Schmidhuber J. PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem. Frontiers in Psychology. 4: 313. PMID 23761771 DOI: 10.3389/Fpsyg.2013.00313 |
0.377 |
|
2013 |
Luciw M, Kompella V, Kazerounian S, Schmidhuber J. An intrinsic value system for developing multiple invariant representations with incremental slowness learning. Frontiers in Neurorobotics. 7: 9. PMID 23755011 DOI: 10.3389/Fnbot.2013.00009 |
0.446 |
|
2013 |
Srivastava RK, Steunebrink BR, Schmidhuber J. First experiments with POWERPLAY. Neural Networks : the Official Journal of the International Neural Network Society. 41: 130-6. PMID 23465562 DOI: 10.1016/J.Neunet.2013.01.022 |
0.367 |
|
2013 |
Ruhrmair U, Solter J, Sehnke F, Xu X, Mahmoud A, Stoyanova V, Dror G, Schmidhuber J, Burleson W, Devadas S. PUF Modeling Attacks on Simulated and Silicon Data Ieee Transactions On Information Forensics and Security. 8: 1876-1891. DOI: 10.1109/Tifs.2013.2279798 |
0.326 |
|
2012 |
Kompella VR, Luciw M, Schmidhuber J. Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams. Neural Computation. 24: 2994-3024. PMID 22845826 DOI: 10.1162/Neco_A_00344 |
0.374 |
|
2012 |
Pape L, Oddo CM, Controzzi M, Cipriani C, Förster A, Carrozza MC, Schmidhuber J. Learning tactile skills through curious exploration. Frontiers in Neurorobotics. 6: 6. PMID 22837748 DOI: 10.3389/Fnbot.2012.00006 |
0.429 |
|
2012 |
Cireşan D, Meier U, Masci J, Schmidhuber J. Multi-column deep neural network for traffic sign classification. Neural Networks : the Official Journal of the International Neural Network Society. 32: 333-8. PMID 22386783 DOI: 10.1016/J.Neunet.2012.02.023 |
0.365 |
|
2012 |
Leitner J, Harding S, Frank M, Forster A, Schmidhuber J. Learning spatial object localization from vision on a humanoid robot International Journal of Advanced Robotic Systems. 9: 243. DOI: 10.5772/54657 |
0.396 |
|
2012 |
Danafar S, Giusti A, Schmidhuber J. Erratum to: Novel Kernel-Based Recognizers of Human Actions Eurasip Journal On Advances in Signal Processing. 2012: 124. DOI: 10.1186/1687-6180-2012-124 |
0.363 |
|
2012 |
Glasmachers T, Koutník J, Schmidhuber J. Kernel representations for evolving continuous functions Evolutionary Intelligence. 5: 171-187. DOI: 10.1007/S12065-012-0070-Y |
0.699 |
|
2011 |
Gagliolo M, Schmidhuber J. Algorithm portfolio selection as a bandit problem with unbounded losses Annals of Mathematics and Artificial Intelligence. 61: 49-86. DOI: 10.1007/S10472-011-9228-Z |
0.408 |
|
2011 |
Graziano V, Koutník J, Schmidhuber J. Unsupervised modeling of partially observable environments Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6911: 503-515. DOI: 10.1007/978-3-642-23780-5_42 |
0.338 |
|
2010 |
Cireşan DC, Meier U, Gambardella LM, Schmidhuber J. Deep, big, simple neural nets for handwritten digit recognition. Neural Computation. 22: 3207-20. PMID 20858131 DOI: 10.1162/Neco_A_00052 |
0.335 |
|
2010 |
Sehnke F, Osendorfer C, Rückstiess T, Graves A, Peters J, Schmidhuber J. Parameter-exploring policy gradients. Neural Networks : the Official Journal of the International Neural Network Society. 23: 551-9. PMID 20061118 DOI: 10.1016/J.Neunet.2009.12.004 |
0.354 |
|
2010 |
Rückstieß T, Sehnke F, Schaul T, Wierstra D, Sun Y, Schmidhuber J. Exploring parameter space in reinforcement learning Paladyn. 1: 14-24. DOI: 10.2478/S13230-010-0002-4 |
0.401 |
|
2010 |
Danafar S, Giusti A, Schmidhuber J. Novel kernel-based recognizers of human actions Eurasip Journal On Advances in Signal Processing. 2010: 202768. DOI: 10.1155/2010/202768 |
0.336 |
|
2010 |
Schmidhuber J. Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010) Ieee Transactions On Autonomous Mental Development. 2: 230-247. DOI: 10.1109/Tamd.2010.2056368 |
0.381 |
|
2010 |
Wierstra D, Förster A, Peters J, Schmidhuber J. Recurrent policy gradients Logic Journal of the Igpl. 18: 620-634. DOI: 10.1093/Jigpal/Jzp049 |
0.379 |
|
2010 |
Schmidhuber J. The new AI is general and mathematically rigorous Frontiers of Electrical and Electronic Engineering in China. 5: 347-362. DOI: 10.1007/S11460-010-0105-Z |
0.364 |
|
2009 |
Graves A, Liwicki M, Fernández S, Bertolami R, Bunke H, Schmidhuber J. A novel connectionist system for unconstrained handwriting recognition. Ieee Transactions On Pattern Analysis and Machine Intelligence. 31: 855-68. PMID 19299860 DOI: 10.1109/Tpami.2008.137 |
0.344 |
|
2009 |
Martín-Guerrero JD, Gomez F, Soria-Olivas E, Schmidhuber J, Climente-Martí M, Jiménez-Torres NV. A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients Expert Systems With Applications. 36: 9737-9742. DOI: 10.1016/J.Eswa.2009.02.041 |
0.347 |
|
2007 |
Schmidhuber J, Wierstra D, Gagliolo M, Gomez F. Training recurrent networks by Evolino. Neural Computation. 19: 757-79. PMID 17298232 DOI: 10.1162/Neco.2007.19.3.757 |
0.339 |
|
2006 |
Schmidhuber J. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts Connection Science. 18: 173-187. DOI: 10.1080/09540090600768658 |
0.372 |
|
2006 |
Gagliolo M, Schmidhuber J. Learning dynamic algorithm portfolios Annals of Mathematics and Artificial Intelligence. 47: 295-328. DOI: 10.1007/S10472-006-9036-Z |
0.401 |
|
2005 |
Graves A, Schmidhuber J. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks : the Official Journal of the International Neural Network Society. 18: 602-10. PMID 16112549 DOI: 10.1016/J.Neunet.2005.06.042 |
0.346 |
|
2004 |
Milano M, Koumoutsakos P, Schmidhuber J. Self-organizing nets for optimization. Ieee Transactions On Neural Networks / a Publication of the Ieee Neural Networks Council. 15: 758-65. PMID 15384562 DOI: 10.1109/Tnn.2004.826132 |
0.375 |
|
2004 |
Graves A, Eck D, Beringer N, Schmidhuber J. Biologically plausible speech recognition with LSTM neural nets Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3141: 127-136. DOI: 10.1007/978-3-540-27835-1_10 |
0.35 |
|
2003 |
Pérez-Ortiz JA, Gers FA, Eck D, Schmidhuber J. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks : the Official Journal of the International Neural Network Society. 16: 241-50. PMID 12628609 DOI: 10.1016/S0893-6080(02)00219-8 |
0.302 |
|
2003 |
Gers FA, Schraudolph NN, Schmidhuber J. Learning precise timing with lstm recurrent networks Journal of Machine Learning Research. 3: 115-143. DOI: 10.1162/153244303768966139 |
0.433 |
|
2002 |
Schmidhuber J, Gers F, Eck D. Learning nonregular languages: a comparison of simple recurrent networks and LSTM. Neural Computation. 14: 2039-41. PMID 12184841 DOI: 10.1162/089976602320263980 |
0.325 |
|
2002 |
Schmidhuber J. Hierarchies Of Generalized Kolmogorov Complexities And Nonenumerable Universal Measures Computable In The Limit International Journal of Foundations of Computer Science. 13: 587-612. DOI: 10.1142/S0129054102001291 |
0.315 |
|
2002 |
Eck D, Schmidhuber J. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks Neural Networks For Signal Processing - Proceedings of the Ieee Workshop. 2002: 747-756. DOI: 10.1109/NNSP.2002.1030094 |
0.309 |
|
2001 |
Kwee I, Schmidhuber J. Optimal Control Using the Transport Equation: The Liouville Machine Adaptive Behavior. 9: 105-118. DOI: 10.1177/105971230200900201 |
0.305 |
|
2000 |
Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Computation. 12: 2451-71. PMID 11032042 DOI: 10.1162/089976600300015015 |
0.322 |
|
1999 |
Hochreiter S, Schmidhuber J. Feature extraction through LOCOCODE Neural Computation. 11: 679-714. PMID 10085426 DOI: 10.1162/089976699300016629 |
0.642 |
|
1999 |
Wiering M, Sałustowicz R, Schmidhuber J. Reinforcement Learning Soccer Teams with Incomplete World Models Autonomous Robots. 7: 77-88. DOI: 10.1023/A:1008921914343 |
0.467 |
|
1998 |
Sałustowicz RP, Wiering MA, Schmidhuber J. Learning Team Strategies: Soccer Case Studies Machine Learning. 33: 263-282. DOI: 10.1023/A:1007570708568 |
0.441 |
|
1998 |
Wiering M, Schmidhuber J. Fast Online Q(λ) Machine Learning. 33: 105-115. DOI: 10.1023/A:1007562800292 |
0.349 |
|
1997 |
Schmidhuber J. Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability. Neural Networks : the Official Journal of the International Neural Network Society. 10: 857-873. PMID 12662875 DOI: 10.1016/S0893-6080(96)00127-X |
0.41 |
|
1997 |
Hochreiter S, Schmidhuber J. Long short-term memory Neural Computation. 9: 1735-1780. PMID 9377276 DOI: 10.1162/Neco.1997.9.8.1735 |
0.675 |
|
1997 |
Schmidhuber J. Low-Complexity Art Leonardo. 30: 97-103. DOI: 10.2307/1576418 |
0.328 |
|
1997 |
Wiering M, Schmidhuber J. HQ-Learning Adaptive Behavior. 6: 219-246. DOI: 10.1177/105971239700600202 |
0.302 |
|
1997 |
Schmidhuber J, Zhao J, Wiering M. Shifting Inductive Bias with Success-Story Algorithm, AdaptiveLevin Search, and Incremental Self-Improvement Machine Learning. 28: 105-130. DOI: 10.1023/A:1007383707642 |
0.367 |
|
1996 |
Schmidhuber J, Eldracher M, Foltin B. Semilinear predictability minimization produces well-known feature detectors Neural Computation. 8: 773-786. DOI: 10.1162/Neco.1996.8.4.773 |
0.335 |
|
1992 |
Schmidhuber J. Learning factorial codes by predictability minimization Neural Computation. 4: 863-879. DOI: 10.1162/Neco.1992.4.6.863 |
0.382 |
|
1992 |
Schmidhuber J. A fixed size storage O(n 3 ) time complexity learning algorithm for fully recurrent continually running networks Neural Computation. 4: 243-248. DOI: 10.1162/Neco.1992.4.2.243 |
0.381 |
|
1992 |
Schmidhuber J. Learning complex, extended sequences using the principle of history compression Neural Computation. 4: 234-242. DOI: 10.1162/Neco.1992.4.2.234 |
0.423 |
|
1992 |
Schmidhuber J. Learning to control fast-weight memories: an alternative to dynamic recurrent networks Neural Computation. 4: 131-139. DOI: 10.1162/Neco.1992.4.1.131 |
0.421 |
|
1991 |
Schmidhuber J, Huber R. Learning To Generate Artificial Fovea Trajectories For Target Detection International Journal of Neural Systems. 2: 125-134. DOI: 10.1142/S012906579100011X |
0.394 |
|
1989 |
Schmidhuber J. A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks Connection Science. 1: 403-412. DOI: 10.1080/09540098908915650 |
0.438 |
|
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