270.
F. Faccio, V. Herrmann, A. Ramesh, L. Kirsch, J. Schmidhuber.
Goal-Conditioned Generators of Deep Policies.
AAAI Conference on Artificial Intelligence (AAAI), 2023.
Preprint: arxiv.org/abs/2207.01570.
269.
A. Ramesh, L. Kirsch, S. van Steenkiste, J. Schmidhuber.
Exploring through Random Curiosity with General Value Functions.
Advances in Neural Information Processing Systems (NeurIPS), New Orleans, 2022.
Preprint: arXiv:2211.10282.
268.
K. Irie, F. Faccio, J. Schmidhuber.
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules.
Advances in Neural Information Processing Systems (NeurIPS), New Orleans, 2022.
Preprint: arXiv:2206.01649.
267.
K. Irie, I. Schlag, R. Csordas, J. Schmidhuber.
A Modern Self-Referential Weight Matrix That Learns to Modify Itself.
International Conference on Machine Learning (ICML), 2022.
Preprint: arXiv:2202.05780.
266.
K. Irie, R. Csordas, J. Schmidhuber.
The Dual Form of Neural Networks Revisited:
Connecting Test Time Predictions to Training Patterns via Spotlights of Attention.
International Conference on Machine Learning (ICML), 2022.
Preprint: arXiv:2202.05798.
265.
R. Csordas, K. Irie, J. Schmidhuber.
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations.
EMNLP 2022, Abu Dhabi, 2022.
Preprint: arXiv:2210.06350.
264.
R. Csordas, K. Irie, J. Schmidhuber. The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization.
International Conference on Learning Representations (ICLR), 2022.
Preprint: arXiv:2110.07732.
263.
M. Strupl, F. Faccio, D. R. Ashley, R. K. Srivastava, J. Schmidhuber. Reward-Weighted Regression Converges to a Global Optimum. AAAI Conference on Artificial Intelligence (AAAI), 2022.
Preprint: arxiv.org/abs/2107.09088.
262.
L. Kirsch, J. Schmidhuber. Meta Learning Backpropagation And Improving It.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
Preprint: arXiv:2012.14905.
261.
K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. Going Beyond Linear Transformers with Recurrent Fast Weight Programmers.
Advances in Neural Information Processing Systems (NeurIPS), 2021.
Preprint: arXiv:2106.06295 . See also the
Blog Post.
260.
R. Csordas, K. Irie, J. Schmidhuber. The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. EMNLP 2021.
Preprint: arXiv:2108.12284.
259.
K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. A Modern Self-Referential Weight Matrix That Learns to Modify Itself.
NeurIPS 2021 WS on Reinforcement Learning.
258.
A. Ramesh, L. Kirsch, S. v. Steenkiste, J. Schmidhuber. Exploring through Random Curiosity with General Value Functions. NeurIPS 2021 WS on Deep Reinforcement Learning, 2021.
257.
I. Schlag, K. Irie, J. Schmidhuber. Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021.
Preprint: arXiv:2102.11174. See also the
Blog Post.
256.
I. Schlag, T. Munkhdalai, J. Schmidhuber. Learning Associative Inference Using Fast Weight Memory. International Conference on Learning Representations (ICLR 2021).
Preprint: arXiv:2011.07831.
255.
F. Faccio, L. Kirsch, J. Schmidhuber. Parameter-based Value Functions. International Conference on Learning Representations (ICLR 2021).
Preprint: arXiv:2006.09226.
254.
A. Gopalakrishnan, S. v. Steenkiste, J. Schmidhuber. Unsupervised Object Keypoint Learning using Local Spatial Predictability. International Conference on Learning Representations (ICLR 2021).
Preprint: arXiv:2011.12930.
253.
R. Csordas, S. v. Steenkiste, J. Schmidhuber. Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. International Conference on Learning Representations (ICLR 2021).
Preprint: arXiv:2010.02066.
252.
D. Miladinovic, A. Stanic, S. Bauer, J. Schmidhuber, J. Buhmann. Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. International Conference on Learning Representations (ICLR 2021).
Link.
251.
A. Stanic, S. v. Steenkiste, J. Schmidhuber. Hierarchical Relational Inference.
35th AAAI Conference on Artificial Intelligence (AAAI 2021).
Preprint: arXiv:2010.03635.
250.
L. Kirsch, J. Schmidhuber. Meta Learning Backpropagation And Improving It. NeurIPS 2020 WS on Metalearning.
Preprint: arXiv:2012.14905.
249.
L. Kirsch, S. v. Steenkiste, J. Schmidhuber. Improving Generalization in Meta Reinforcement Learning using Neural Objectives. International Conference on Learning Representations (ICLR 2020).
Preprint: arXiv:1910.04098.
248.
M. Wand, J. Schmidhuber. Fusion Architectures for Word-based Audiovisual Speech Recognition.
Proc. of the Annual Conference of the International Speech Communication Association (Interspeech), 2020.
247.
L. Tuggener, Y. P. Satyawan, A. Pacha, J. Schmidhuber, T. Stadelmann.
The DeepScoresV2 Dataset and Benchmark for
Music Object Detection.
25th International Conference on Pattern Recognition (ICPR), 2020.
246.
M. Riva, M. Wand, J. Schmidhuber. Motion dynamics improve speaker-independent lipreading.
Proc. 45th IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2020).
245.
S. v. Steenkiste, F. Locatello, J. Schmidhuber, O. Bachem. Are Disentangled Representations Helpful for Abstract Visual Reasoning? Advances in Neural Information Processing Systems (NIPS), Vancouver, 2019.
Preprint: arxiv:1905.12506
245.
P. Rauber, A. Ummadisingu, F. Mutz, J. Schmidhuber.
Hindsight policy gradients.
International Conference on Learning Representations (ICLR 2019).
Preprint: arXiv:1612.07771.
244.
R. Csordas, J. Schmidhuber.
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control.
International Conference on Learning Representations (ICLR 2019).
PDF.
243.
I. Schlag, J. Schmidhuber. Learning to Reason with Third Order Tensor Products. Advances in Neural Information Processing Systems (NIPS), Montreal, 2018.
Preprint: arXiv:1811.12143.
242.
D. Ha, J. Schmidhuber. Recurrent World Models Facilitate Policy Evolution. Advances in Neural Information Processing Systems (NIPS), Montreal, 2018. (Talk.)
Preprint: arXiv:1809.01999.
241.
S. v. Steenkiste, M. Chang, K. Greff, J. Schmidhuber.
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions.
International Conference on Learning Representations (ICLR), 2018.
Link.
240.
M. Wand, T. Schultz, J. Schmidhuber. Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. Proc. of the Annual Conference of the International Speech Communication Association (Interspeech), 2018.
239.
Investigations on End-to-End Audiovisual Fusion.
M. Wand, N. Thang Vu, J. Schmidhuber.
Proc. 43rd IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018), p 3041-3045, 2018.
238.
L. Tuggener, I. Elezi, J. Schmidhuber, M. Pelillo, T. Stadelmann.
DeepScores - A dataset for segmentation, detection and classification of tiny objects.
ICPR 2018.
237.
K. Greff, S. v. Steenkiste, J. Schmidhuber. Neural Expectation Maximization. Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017.
Preprint: arXiv:1708.03498.
236.
J. G. Zilly, R. K. Srivastava, J. Koutnik and J. Schmidhuber.
Recurrent Highway Networks.
International Conference on Machine Learning (ICML 2017).
Preprint: arXiv:1607.03474.
235.
K. Greff, R. K. Srivastava and J. Schmidhuber.
Highway and Residual Networks learn Unrolled Iterative Estimation.
International Conference on Learning Representations (ICLR 2017).
Preprint: arXiv:1612.07771.
234.
M. Wand, J. Schmidhuber. Improving Speaker-Independent Lipreading with Domain-Adversarial Training. Proc. of the 18th Annual Conference of the International Speech Communication Association (Interspeech), 2017, pp. 3662-3666.
233.
K. Greff, A. Rasmus, M. Berglund, T. H. Hao, J. Schmidhuber, and H. Valpola. Tagger: Deep unsupervised
perceptual grouping. NIPS 2016.
Preprint: arxiv:1606.06724.
232.
M. Wand, J. Schmidhuber. Deep Neural Network Frontend for Continuous EMG-based Speech Recognition. Proc. of the 17th Annual Conference of the International Speech Communication Association (Interspeech), 2016.
231.
M. Wand, J. Koutnik, J. Schmidhuber. Lipreading with Long Short-Term Memory. Proc. ICASSP, p 6115-6119, 2016.
230.
S. van Steenkiste, J. Koutnik, K.Driessens, J. Schmidhuber. A Wavelet-based Encoding for Neuroevolution. In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (GECCO, pp. 517-524). ACM, July 2016.
229.
K. Greff, R. K. Srivastava, J. Schmidhuber. Training Very Deep Networks. Advances in Neural Information Processing Systems (NIPS), 2015.
Preprint: arxiv:1505.00387.
228.
M. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber. Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. Advances in Neural Information Processing Systems (NIPS), 2015.
Preprint: arxiv:1506.07452.
227.
R. K. Srivastava, J. Masci, F. Gomez, J. Schmidhuber: Understanding Locally Competitive Networks. International Conference on Learning Representations ICLR 2015.
Preprint: arxiv:1410.1165.
226.
E. Nivel, K.R. Thorisson, B.R. Steunebrink, J. Schmidhuber. Anytime Bounded Rationality. In Proceedings of the 8th Conference on Artificial General Intelligence (AGI 2015), LNAI 9205, pages 121-130. Springer, Heidelberg, 2015.
225.
M. Stollenga, J.Masci, F. Gomez, J. Schmidhuber.
Deep Networks with Internal Selective Attention through Feedback Connections.
Preprint arXiv:1407.3068 [cs.CV].
Advances in Neural Information Processing Systems (NIPS), 2014.
224. J. Koutnik, K. Greff, F. Gomez, J. Schmidhuber. A Clockwork RNN. Proc. 31st International Conference on Machine Learning (ICML), p. 1845-1853, Beijing, 2014. Preprint arXiv:1402.3511 [cs.NE].
223.
J. Koutnik, J. Schmidhuber, F. Gomez. Evolving Deep Unsupervised Convolutional Networks for Vision-Based Reinforcement Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Vancouver, CA, 2014.
222.
V. R. Kompella, M. Stollenga, M. Luciw, J. Schmidhuber. Explore to See, Learn to Perceive, Get the Actions for Free: SKILLABILITY. Proc. IEEE International Joint Conference on Neural Networks (IJCNN), Beijing, 2014.
221.
J. Leitner, A. Foerster, J. Schmidhuber.
Improving Robot Vision Models for Object Detection Through Interaction. Proc. IEEE International Joint Conference on Neural Networks (IJCNN), Beijing, 2014.
220.
J. Koutnik, J. Schmidhuber, F. Gomez. Online Evolution of Deep Convolutional Networks for Reinforcement Learning. In Proceedings of the Simulation of Adaptive Behavior Conference (SAB), Castellon, Spain, 2014.
219.
M. Stollenga, J. Schmidhuber, F. Gomez (2014). Rapid Humanoid Motion Learning through Coordinated, Parallel Evolution. In Proceedings of the Simulation of Adaptive Behavior Conference (SAB), Castellon, Spain, 2014.
218.
V. R. Kompella, S. Kazerounian, J. Schmidhuber. An Anti-Hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning. Proc. International Conference on Simulation of Adaptive Behavior (SAB), Castellon, 2014.
217.
E. Nivel, K. R. Thorisson, B. Steunebrink, H. Dindo, G. Pezzulo, M. Rodriguez, C. Hernandez, D. Ognibene, J. Schmidhuber, R. Sanz, H. P. Helgason, A. Chella, G. K. Jonsson.
Autonomous Acquisition of Natural Language.
Proceedings of IADIS International Conference on Intelligent Systems \& Agents
Lisbon, Portugal, July 15-17, p 58-66, 2014.
216.
E. Nivel, K. R. Thorisson, B. Steunebrink, H. Dindo, G. Pezzulo, M. Rodriguez, C. Hernandez, D. Ognibene, J. Schmidhuber, R. Sanz, H. P. Helgason, A. Chella. Bounded Seed-AGI. In Proceedings of the 7th Conference on Artificial General Intelligence (AGI 2014). Springer, Heidelberg, 2014.
215.
J. Leitner, M. Frank, A. Foerster, J. Schmidhuber.
Reactive Reaching and Grasping on a Humanoid: Towards Closing the Action-Perception Loop on the iCub. International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2014.
214.
J. Leitner, M. Luciw, A. Foerster, J. Schmidhuber.
Teleoperation of a 7 DOF Humanoid Robot Arm Using Human Arm Accelerations and EMG Signals. International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2014
213.
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, J. Schmidhuber. Compete to Compute. In Proc. Neural Information Processing Systems (NIPS) 2013, Lake Tahoe.
212.
H. Ngo, M. Luciw, V. Ngo, J. Schmidhuber. Upper Confidence Weighted Learning for Efficient Exploration in Multiclass Prediction with Binary Feedback. International Joint Conference on Artificial Intelligence IJCAI 2013, Beijing, China.
PDF.
211.
D. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber. Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks. MICCAI 2013.
PDF.
210.
A. Giusti, D. Ciresan, J. Masci, L.M. Gambardella, J. Schmidhuber. Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks. ICIP 2013.
Preprint arXiv:1302.1700
209.
J. Masci, A. Giusti, D. Ciresan, G. Fricout, J. Schmidhuber. A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks. ICIP 2013. Preprint arXiv:1302.1690
208.
J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez.
Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning.
In Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO), Amsterdam, 2013.
PDF.
207.
B. Steunebrink, J. Koutnik, K. R. Thorisson, E. Nivel, J. Schmidhuber. Resource-Bounded Machines are Motivated to be Effective, Efficient, and Curious. In K.-U. Kuehneberger, S. Rudolph, and P. Wang, editors, Proceedings of the 6th Conference on Artificial General Intelligence (AGI 2013), LNAI 7999, p. 119-129. Springer, Heidelberg.
PDF.
AGI 2013 best paper award (Kurzweil Prize).
206.
Yi Sun, F. Gomez, T. Schaul, J. Schmidhuber.
A Linear Time Natural Evolution Strategy for Non-Separable Functions.
In Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO), Amsterdam, 2013.
Preprint arXiv:1106.1998v2 (2011).
205.
Task-Relevant Roadmaps: A Framework for Humanoid Motion Planning.
M. Stollenga, L. Pape, M. Frank, J. Leitner, A. Förster, J. Schmidhuber.
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
Tokyo, Japan, 2013.
PDF.
204.
Artificial Neural Networks For Spatial Perception: Towards Visual Object Localisation in Humanoid Robots.
J. Leitner, S. Harding, M. Frank, A. Förster, J. Schmidhuber.
International Joint Conference on Neural Networks (IJCNN), Dallas, USA, 2013.
PDF.
203.
J. Leitner, S. Harding, M. Frank, A. Förster, J. Schmidhuber.
Humanoid Learns to Detect Its Own Hands.
IEEE Congress on Evolutionary Computing (CEC), Cancun, Mexico, 2013.
PDF.
202.
J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez.
Evolving Large-Scale Neural Networks for Vision-Based TORCS.
In Foundations of Digital Games (FDG), Chania, Crete, 2013.
PDF.
201.
D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber.
Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images.
In Advances in Neural Information Processing Systems (NIPS 2012), Lake Tahoe,
2012. PDF.
200.
Sun Yi, F. Gomez, J. Schmidhuber.
On the Size of the Online Kernel Sparsification Dictionary.
Proc. International Conference on Machine Learning ICML 2012,
Edinburgh.
PDF.
199.
D. Ciresan, U. Meier, J. Schmidhuber.
Multi-column Deep Neural Networks for Image Classification.
Proc. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012,
p 3642-3649, 2012.
PDF.
Longer Tech Report:
arXiv:1202.2745v1 [cs.CV]
198.
J. Leitner, S. Harding, M. Frank, A. Foerster, J. Schmidhuber.
Transferring Spatial Perception Between Robots Operating In A Shared Workspace.
Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS'12), Vilamoura, 2012. PDF.
197.
L. Gisslen, M. Ring, M. Luciw, J. Schmidhuber.
Modular Value Iteration Through Regional Decomposition.
In Proc. Fifth Conference on Artificial General Intelligence (AGI-12),
Oxford, UK, 2012. PDF.
196.
J. Nagi, H. Ngo, A. Giusti, L. M. Gambardella, J. Schmidhuber, G. A. Di Caro. Incremental Learning using Partial Feedback for Gesture-based Human-Swarm Interaction. Proc. of the 21st IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 898-905, Paris, France, 2012.
PDF.
195.
J. Leitner, S. Harding, A. Foerster, J. Schmidhuber.
Mars Terrain Image Classification using Cartesian Genetic Programming.
11th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS). Torino, Italy, 2012. PDF.
194.
M. Frank, J. Leitner, M. Stollenga, G. Kaufmann, S. Harding, A. Fˆrster, J. Schmidhuber.
The Modular Behavioral Environment for Humanoids & other Robots (MoBeE).
9th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Rome, Italy, 2012. PDF.
193.
J. Leitner, P. Chandrashekhariah, S. Harding, M. Frank, G. Spina, A. Foerster, J. Triesch, J. Schmidhuber.
Autonomous Learning Of Robust Visual Object Detection and Identification on a Humanoid.
Proc. IEEE Conference on Development and Learning / EpiRob 2012
(ICDL-EpiRob'12), San Diego, 2012.
PDF.
Paper of Excellence Award.
192.
V. R. Kompella, M. Luciw, M. Stollenga, L. Pape, J. Schmidhuber.
Autonomous Learning of Abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis.
Proc. IEEE Conference on Development and Learning / EpiRob 2012
(ICDL-EpiRob'12), San Diego, 2012.
191.
R. K. Srivastava, B. Steunebrink, M. Stollenga, J. Schmidhuber
Continually Adding Self-Invented Problems to the Repertoire: First Experiments with PowerPlay.
Proc. IEEE Conference on Development and Learning / EpiRob 2012
(ICDL-EpiRob'12), San Diego, 2012. PDF.
190.
S. Kazerounian, M. Luciw, Y. Sandamirskaya, M. Richter, J. Schmidhuber, G. Schoener.
Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics.
Proc. IEEE Conference on Development and Learning / EpiRob 2012
(ICDL-EpiRob'12), San Diego, 2012.
189.
M. Luciw, J. Schmidhuber.
Low Complexity Proto-Value Function Updating with Incremental Slow Feature Analysis.
Proc. International Conference on Artificial Neural Networks (ICANN 2012), Lausanne, 2012.
PDF.
188.
R. K. Srivastava, F. Gomez, J. Schmidhuber.
Generalized Compressed Network Search.
In C. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, M. Pavone, eds.,
12th Int. Conf. on Parallel Problem Solving from Nature - PPSN XII,
Taormina, 2012.
PDF.
187.
F. Gomez, J. Koutnik, J. Schmidhuber.
Compressed Network Complexity Search.
In C. Coello Coello, V. Cutello, K. Deb, S. Forrest, G. Nicosia, M. Pavone, eds.,
12th Int. Conf. on Parallel Problem Solving from Nature - PPSN XII,
Taormina, 2012. Nominated for best paper award.
PDF.
186.
H. Ngo, M. Luciw, A. Foerster, J. Schmidhuber.
Learning Skills from Play: Artificial Curiosity on a Katana Robot Arm.
Proc. IJCNN 2012.
PDF.
Video.
185.
J. Masci, U. Meier, D. Ciresan, G. Fricout, J. Schmidhuber
Steel Defect Classification with Max-Pooling Convolutional Neural Networks.
Proc. IJCNN 2012.
PDF.
184.
D. Ciresan, U. Meier, J. Schmidhuber.
Transfer Learning for Latin and Chinese Characters with Deep Neural Networks.
Proc. IJCNN 2012, p 1301-1306, 2012.
PDF.
183.
F. Gomez, J. Koutnik, J. Schmidhuber.
Complexity Search for Compressed Neural Networks.
Proc. GECCO 2012.
PDF.
182.
J. Leitner, S. Harding, M. Frank, A. Foerster, J. Schmidhuber.
icVision: A Modular Vision System for Cognitive Robotics Research.
5th International Conference on Cognitive Systems (CogSys). Vienna, Austria, 2012.
181.
R. K. Srivastava, J. Schmidhuber, F. Gomez.
Generalized Compressed Network Search.
Proc. GECCO 2012.
PDF.
180.
S. Harding, V. Graziano, J. Leitner, J. Schmidhuber.
MT-CGP: Mixed Type Cartesian Genetic Programming.
Proc. GECCO 2012.
PDF.
179.
V. R. Kompella, M. Luciw, J. Schmidhuber.
Incremental Slow Feature Analysis.
International Joint Conference on Artificial Intelligence (IJCAI-2011, Barcelona), 2011.
PDF.
178.
D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber.
Flexible, High Performance Convolutional Neural Networks for Image Classification.
International Joint Conference on Artificial Intelligence (IJCAI-2011, Barcelona), 2011.
ArXiv preprint.
177.
Yi Sun, F. Gomez, M. Ring, J. Schmidhuber. Incremental Basis Construction from Temporal Difference Error. Proceedings of the 28th International Conference on Machine Learning (ICML-11), 2011.
PDF.
176.
V. R. Kompella, L. Pape, J. Masci, M. Frank and J. Schmidhuber. AutoIncSFA and Vision-based Developmental Learning for Humanoid Robots. 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, 2011.
175.
J. Nagi, F. Ducatelle, G. A. Di Caro, D. Ciresan, U. Meier, A. Giusti, F. Nagi, J. Schmidhuber, L. M. Gambardella. Max-Pooling Convolutional Neural Networks for Vision-based Hand Gesture Recognition. IEEE International Conference on Signal and Image Processing Applications, 2011.
PDF.
174.
M. Frank, A. Förster, J. Schmidhuber. Reflexive Collision Response with Virtual Skin.
International Conference on Agents and Artificial Intelligence ICAART 2012, accepted 2011.
173.
V. Graziano, J. Koutnik, J. Schmidhuber.
Unsupervised Modeling of Partially Observable Environments.
22nd European Conference on Machine Learning ECML, Athens,
2011.
PDF.
172.
U. Meier, D. C. Ciresan, L. M. Gambardella, J. Schmidhuber.
Better Digit Recognition with a Committee of Simple Neural Nets.
11th International Conference on Document Analysis and Recognition (ICDAR 2011),
Beijing, China, 2011.
PDF.
171.
D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber.
Convolutional Neural Network Committees For Handwritten Character Classification.
11th International Conference on Document Analysis and Recognition (ICDAR 2011),
Beijing, China, 2011.
PDF.
170.
T. Schaul, T. Glasmachers, J. Schmidhuber.
High Dimensions and Heavy Tails for Natural Evolution Strategies.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2011, Dublin), 2011.
PDF.
169.
T. Schaul, Yi Sun, D. Wierstra, F. Gomez, J. Schmidhuber. Curiosity-Driven Optimization. IEEE Congress on Evolutionary Computation (CEC-2011), 2011.
PDF.
168.
D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber.
A Committee of Neural Networks for Traffic Sign Classification.
International Joint Conference on Neural Networks (IJCNN-2011, San Francisco), 2011.
PDF.
167.
L. Pape, F. Gomez, M. Ring, J. Schmidhuber.
Modular deep belief networks that do not forget.
International Joint Conference on Neural Networks (IJCNN-2011, San Francisco), 2011.
PDF.
166.
J. Masci, D. C. Ciresan, U. Meier, J. Schmidhuber.
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction.
International Conference on Artificial Neural Networks (ICANN-2011, Espoo, Finland), 2011.
PDF.
165.
M. Ring, T. Schaul, J. Schmidhuber.
The Two-Dimensional Organization of Behavior.
In Proc. Joint IEEE International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (ICDL-EpiRob 2011), Frankfurt, 2011.
PDF.
164.
G. Cuccu, M. Luciw, J. Schmidhuber, F. Gomez.
Intrinsically Motivated Evolutionary Search for Vision-Based Reinforcement Learning.
In Proc. Joint IEEE International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (ICDL-EpiRob 2011), Frankfurt, 2011.
PDF.
163.
H. Ngo, M. Ring, J. Schmidhuber.
Curiosity Drive based on Compression Progress for Learning Environment Regularities.
In Proc. Joint IEEE International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (ICDL-EpiRob 2011), Frankfurt, 2011.
162.
M. Luciw, V. Graziano, M. Ring, J. Schmidhuber.
Artificial Curiosity with Planning for Autonomous Visual and Perceptual Development.
In Proc. Joint IEEE International Conference on Development and Learning (ICDL) and on Epigenetic Robotics (ICDL-EpiRob 2011), Frankfurt, 2011.
PDF.
161.
J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, A. Graves.
On Fast Deep Nets for AGI Vision.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
160.
Sun Yi, F. Gomez, J. Schmidhuber.
Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
159.
T. Glasmachers, J. Schmidhuber.
Optimal Direct Policy Search.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
158.
L. Gisslen, M. Luciw, V. Graziano, J. Schmidhuber.
Sequential Constant Size Compressors and Reinforcement Learning.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
Kurzweil Prize for Best AGI Paper 2011.
157.
T. Schaul, L. Pape, T. Glasmachers, V. Graziano J. Schmidhuber.
Coherence Progress: A Measure of Interestingness Based on Fixed Compressors.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
156.
B. Steunebrink, J. Schmidhuber.
A Family of Gödel Machine Implementations.
In Proc. Fourth Conference on Artificial General Intelligence (AGI-11),
Google, Mountain View, California, 2011.
PDF.
155.
S. Yi, F. Gomez, J. Schmidhuber (2010). Improving Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices. In Advances in Neural Information Processing Systems (NIPS 2010), 2010.
PDF.
154.
S. Danafar, A. Gretton, and J. Schmidhuber. Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition. Proc. ECML 2010.
PDF.
153.
T. Glasmachers, T. Schaul, Sun Yi, D. Wierstra, J. Schmidhuber.
Exponential Natural Evolution Strategies.
Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO-2010), Portland, 2010.
PDF.
GECCO 2010 best paper nomination.
152.
J. Koutnik, F. Gomez, J. Schmidhuber (2010). Evolving Neural Networks in Compressed Weight Space. Proceedings of the Genetic and Evolutionary Computation Conference
(GECCO-2010), Portland, 2010.
PDF.
151.
U. Ruehrmair, F. Sehnke, J. Soelter, S. Devadas, and J. Schmidhuber. Modeling attacks on physical unclonable functions. In Proceedings of the 17th ACM Conference on Computer and Communications Security, ACM CCS 2010, 2010.
PDF.
150.
S. Yi, T. Glasmachers, T. Schaul, J. Schmidhuber.
Frontier Search.
The 3rd Conference on Artificial General Intelligence (AGI-10), 2010.
PDF. AGI 2010 best paper award (Kurzweil Prize).
149.
T. Glasmachers, T. Schaul, J. Schmidhuber.
A Natural Evolution Strategy for Multi-Objective Optimization.
Proceedings of Parallel Problem Solving from Nature (PPSN-2010, Krakow), 2010.
PDF.
148.
F. Sehnke, C. Osendorfer, J. Soelter, J. Schmidhuber, U. Ruehrmair. Policy gradients for cryptanalysis. In W. Duch K. Diamantaras and L. Iliadis, editors, Proceedings of the International Conference on Artificial Neural Networks, ICANN 2010. Springer-Verlag Berlin Heidelberg, 2010.
PDF.
147.
M. Grüttner, F. Sehnke, T. Schaul, J. Schmidhuber.
Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients.
Proceedings of the International Conference on Artificial Neural Networks (ICANN-2010),
Greece, 2010.
PDF.
146.
J. Koutnik, F. Gomez, J. Schmidhuber.
Searching for Minimal Neural Networks in Fourier Space.
The 3rd Conference on Artificial General Intelligence (AGI-10), 2010.
PDF.
145.
T. Schaul, J. Schmidhuber.
Towards Practical Universal Search.
The 3rd Conference on Artificial General Intelligence (AGI-10), 2010.
PDF.
144. J. Schmidhuber. Artificial Scientists & Artists Based on the Formal Theory of Creativity.
In Proceedings of the #rd Conference on Artificial General Intelligence (AGI-10), 2010.
PDF.
143.
S. Yi, D. Wierstra, T. Schaul, J. Schmidhuber.
Efficient Natural Evolution Strategies.
Genetic and Evolutionary Computation Conference (GECCO-09),
Montreal, 2009.
PDF. Best paper award.
142.
S. Yi, D. Wierstra, T. Schaul, J. Schmidhuber.
Stochastic Search using the Natural Gradient.
Proceedings of the 26th International Conference on Machine Learning (ICML-09),
Montreal, 2009.
PDF.
141.
A. Graves, J. Schmidhuber.
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks.
Advances in Neural Information Processing Systems 22, NIPS'22, p 545-552,
Vancouver, MIT Press, 2009.
PDF.
140.
J. Bayer, D. Wierstra, J. Togelius, J. Schmidhuber.
Evolving memory cell structures for sequence learning.
Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09),
Cyprus, 2009.
PDF.
139.
J. Unkelbach, S. Yi, J. Schmidhuber.
An EM based training algorithm for recurrent neural networks.
Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09),
Cyprus, 2009.
PDF.
138.
F. J. Gomez, J. Togelius, J. Schmidhuber.
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning .
Proceedings of the 19th International Conference on Artificial Neural Networks (ICANN-09),
Cyprus, 2009.
PDF.
137.
T. Schaul and J. Schmidhuber. A Scalable Neural Network Architecture for Board Games.
Proceedings of the 19th International Conference on
Artificial Neural Networks (ICANN-09),
Cyprus, 2009.
PDF.
136.
N. v. Hoorn, J. Togelius, J. Schmidhuber.
Hierarchical Controller Learning in a First-Person Shooter.
Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Games,
CIG-2009, p. 294-301, Milano, 2009.
PDF.
135.
J. Togelius, S. Karakovskiy, J. Koutnik, and J. Schmidhuber. Super Mario Evolution.
Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Games
CIG-2009, p. 156-161, Milano, 2009.
PDF.
134.
N. van Hoorn, J. Togelius, D. Wierstra, J. Schmidhuber.
Robust player imitation using multiobjective evolution.
Proceedings of the Congress on Evolutionary Computation (CEC-09),
Trondheim, 2009.
PDF.
133.
A. Graves, S. Fernandez,M. Liwicki, H. Bunke, J. Schmidhuber.
Unconstrained online handwriting recognition with recurrent neural networks.
Advances in Neural Information Processing Systems 21, NIPS'21,
p 577-584, 2008, MIT Press,
Cambridge, MA, 2008.
PDF.
132.
J. Schmidhuber.
Driven by Compression Progress. In
Knowledge-Based Intelligent Information and
Engineering Systems KES-2008,
Lecture Notes in Computer Science LNCS 5177, p 11, Springer, 2008.
(Abstract of invited keynote talk.)
PDF.
131.
T. Rückstiess, M. Felder, J. Schmidhuber.
State-Dependent Exploration for Policy Gradient Methods.
19th European Conference on Machine Learning ECML,
2008.
PDF.
130.
J. Togelius, J. Schmidhuber. An Experiment in Automatic Game Design
Proceedings of the 2008 IEEE Symposium on Computational Intelligence in Games CIG-2008,
Perth, Australia, 2008.
129.
A. Agapitos, J. Togelius, S. Lucas, J. Schmidhuber
Generating Diverse Opponents with Multiobjective Evolution.
Proceedings of the 2008 IEEE Symposium on Computational Intelligence in Games CIG-2008,
Perth, Australia, 2008.
128.
T. Schaul and J. Schmidhuber. A Scalable Neural Network Architecture for Board Games.
Proceedings of the 2008 IEEE Symposium on Computational Intelligence in Games CIG-2008,
Perth, Australia, 2008.
PDF.
127.
M. Gagliolo and J. Schmidhuber.
Distributed Algorithm Portfolios.
International Symposium on Distributed Computing
and Artificial Intelligence 2008 , DCAI 2008
126.
J. Togelius, T. Schaul, J. Schmidhuber, F. Gomez.
Countering Poisonous Inputs with Memetic Neuroevolution.
Proceedings of Parallel Problem Solving from Nature PPSN-2008, Dortmund, 2008.
PDF.
125.
F. Sehnke, C. Osendorfer, T. Rückstiess, A. Graves, J. Peters, and J. Schmidhuber.
Policy gradients with parameter-based exploration for control.
In J. Koutnik V. Kurkova, R. Neruda, editors,
Proceedings of the
International Conference on Artificial Neural Networks ICANN-2008
ICANN 2008, Prague, LNCS 5163, pages 387-396. Springer-Verlag Berlin Heidelberg, 2008.
PDF.
124.
D. Wierstra, T. Schaul, J. Peters, J. Schmidhuber. Episodic Reinforcement
Learning by Logistic Reward-Weighted Regression.
In J. Koutnik V. Kurkova, R. Neruda, editors,
Proceedings of the
International Conference on Artificial Neural Networks ICANN-2008
ICANN 2008, Prague. Springer-Verlag Berlin Heidelberg, 2008.
PDF.
123.
D. Wierstra, T. Schaul, J. Peters, J. Schmidhuber. Fitness Expectation Maximization.
Proceedings of Parallel Problem Solving from Nature PPSN-2008, Dortmund, 2008.
PDF.
122.
D. Wierstra, T. Schaul, J. Peters, J. Schmidhuber.
Natural Evolution Strategies.
Proceedings of IEEE Congress on Evolutionary Computation CEC-2008, Hongkong, 2008.
PDF.
121.
J. Togelius, F. Gomez, J. Schmidhuber.
Learning What to Ignore: Memetic Climbing in Topology and Weight Space.
IEEE WCCI 2008, Hong Kong, 2008.
PDF.
120.
J. Schmidhuber.
Simple Algorithmic Principles of Discovery, Subjective Beauty,
Selective Attention, Curiosity & Creativity.
In V. Corruble, M. Takeda, E. Suzuki, eds.,
Proc. 10th Intl. Conf. on Discovery Science (DS 2007)
p. 26-38, LNAI 4755, Springer, 2007.
Joint invited lecture for DS 2007 and ALT 2007, Sendai, Japan, 2007.
Preprint: arxiv:0709.0674.
PDF.
119.
J. Schmidhuber (see #121 above):
Simple Algorithmic Principles of Discovery, Subjective Beauty,
Selective Attention, Curiosity \& Creativity.
M. Hutter, R. A. Servedio, E. Takimoto, eds.,
Proc. 18th Intl. Conf. on Algorithmic Learning Theory (ALT 2007)
p. 32, LNAI 4754, Springer, 2007.
Joint invited lecture for ALT 2007 and DS 2007.
118.
D. Wierstra, J. Schmidhuber.
Policy Gradient Critics.
18th European Conference on Machine Learning ECML,
Warszaw, 2007.
PDF.
117.
M. Liwicki, A. Graves, H. Bunke, J. Schmidhuber. A novel approach
to on-line handwriting recognition based on bidirectional
long short-term memory networks. 9th International Conference
on Document Analysis and Recognition, 2007.
PDF.
116.
S. Fernandez, A. Graves, J. Schmidhuber. An application of
recurrent neural networks to discriminative keyword
spotting.
Intl. Conf. on Artificial Neural Networks ICANN'07,
2007.
PDF.
115.
A. Graves, S. Fernandez, J. Schmidhuber. Multi-Dimensional Recurrent
Neural Networks.
Intl. Conf. on Artificial Neural Networks ICANN'07,
2007.
Preprint: arxiv:0705.2011.
PDF.
114.
D. Wierstra, A. Foerster, J. Peters, J. Schmidhuber. Solving Deep Memory POMDPs
with Recurrent Policy Gradients.
Intl. Conf. on Artificial Neural Networks ICANN'07,
2007.
PDF.
113.
S. Fernandez, A. Graves, J. Schmidhuber.
Sequence labelling in structured domains with
hierarchical recurrent neural networks. In Proc.
20th International Joint Conference on Artificial Intelligence
(IJCAI 07), p. 774-779, Hyderabad, India, 2007 (talk).
PDF.
112.
M. Gagliolo and J. Schmidhuber.
Learning restart strategies. In M. M. Veloso, ed., Proc.
20th International Joint Conference on Artificial Intelligence
(IJCAI 07), p. 792-797, Hyderabad, India, AAAI Press, 2007 (talk).
PDF.
111.
A. Foerster, A. Graves, J. Schmidhuber.
RNN-based Learning of Compact Maps for Efficient Robot Localization.
15th European Symposium on Artificial Neural Networks,
ESANN, Bruges, Belgium, 2007
PDF.
110.
F. Gomez, J. Schmidhuber, and R. Miikkulainen (2006).
Efficient Non-Linear Control through Neuroevolution.
Proceedings of the European Conference
on Machine Learning (ECML-06, Berlin).
PDF.
109.
H. Mayer, F. Gomez, D. Wierstra, I. Nagy, A. Knoll, and J. Schmidhuber (2006).
A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent
Neural Networks. Proceedings of the International Conference on
Intelligent Robotics and Systems (IROS-06, Beijing).
PDF.
(Best paper nomination.)
108.
A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber.
Connectionist Temporal Classification: Labelling Unsegmented Sequence
Data with Recurrent Neural Networks. Proceedings of
the International Conference on Machine Learning (ICML-06, Pittsburgh), 2006.
PDF.
107.
B. Bakker, V. Zhumatiy, G. Gruener, J. Schmidhuber.
Quasi-Online Reinforcement Learning for Robots.
Proceedings of the International Conference on
Robotics and Automation (ICRA-06), Orlando, Florida, 2006.
PDF.
106.
A. Chernov, J. Schmidhuber.
Prefix-like Complexities and Computability in the Limit.
Proc. of Second Conference on Computability in Europe, CiE 2006, LNCS 3988, pp. 85-93.
Based on TR IDSIA-11-05: PDF.
105.
V. Zhumatiy, F. Gomez, M. Hutter, and J. Schmidhuber. Metric State Space
Reinforcement Learning for a Vision-Capable Mobile Robot.
In Proceedings of the International Conference on
Intelligent Autonomous Systems, IAS-06, Tokyo, 2006.
PDF.
104.
J. Schmidhuber, M. Gagliolo, D. Wierstra, F. Gomez.
Evolino for Recurrent Support Vector Machines.
In Proceedings of the European Symposium on
Artificial Neural Networks (ESANN-06, Bruge), 2006.
Based on TR IDSIA-19-05:
PDF.
103.
M. Gagliolo, J. Schmidhuber. Dynamic Algorithm Portfolios.
AIMATH06, Ninth International Symposium on Artificial
Intelligence and Mathematics, Fort Lauderdale, Florida, 2006.
PDF.
102.
J. Schmidhuber.
Completely Self-Referential Optimal Reinforcement Learners.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3697, pp. 223-233, Springer-Verlag Berlin Heidelberg, 2005
(plenary talk).
PDF.
HTML overview.
101.
J. Schmidhuber and D. Wierstra and F. J. Gomez.
Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Learning.
Proceedings of the 19th International Joint Conference
on Artificial Intelligence (IJCAI), Edinburgh, p. 853-858, 2005.
PDF.
100.
D. Wierstra and F. Gomez and J. Schmidhuber.
Modeling systems with internal state using Evolino.
In Proc. of the 2005 conference on genetic and
evolutionary computation (GECCO), Washington, D. C.,
pp. 1795-1802, ACM Press, New York, NY, USA, 2005.
PDF.
Best paper award.
99.
F. Gomez and J. Schmidhuber.
Co-evolving recurrent neurons learn deep memory POMDPs.
In Proc. of the 2005 conference on genetic and
evolutionary computation (GECCO), Washington, D. C.,
pp. 1795-1802, ACM Press, New York, NY, USA, 2005.
(Nominated for a best paper award).
PDF.
98.
J. Schmidhuber.
A Technical Justification of Consciousness.
Proc. of the 9th annual meeting of the
Association for the Scientific Study of Consciousness,
ASSC9, Caltech, Pasadena, CA, 2005.
97.
F. J. Gomez and J. Schmidhuber.
Evolving modular fast-weight networks for control.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3697, pp. 383-389, Springer-Verlag Berlin Heidelberg, 2005.
PDF.
HTML overview.
96.
A. Graves, S. Fernandez, and J. Schmidhuber.
Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3697, pp. 799-804, Springer-Verlag Berlin Heidelberg, 2005.
PDF.
95.
M. Gagliolo and J. Schmidhuber.
A neural network model for adaptive online time allocation.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3697, pp. 7-12, Springer-Verlag Berlin Heidelberg, 2005.
PDF.
94.
M. v. d. Giessen and J. Schmidhuber.
Fast color-based object recognition independent of position and
orientation.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3696, pp. 469-474, Springer-Verlag Berlin Heidelberg, 2005.
PDF.
93.
N. Beringer and A. Graves and F. Schiel and J. Schmidhuber.
Classifying unprompted speech by retraining LSTM Nets.
In W. Duch et al. (Eds.):
Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,
LNCS 3696, pp. 575-581, Springer-Verlag Berlin Heidelberg, 2005.
PDF.
92.
A. Graves and J. Schmidhuber.
Framewise Phoneme Classification with Bidirectional LSTM Networks.
In Proc. International Joint Conference on Neural Networks IJCNN'05, 2005.
PDF.
91.
J. Schmidhuber.
Self-Motivated Development Through
Rewards for Predictor Errors / Improvements.
In D. Blank and L. Meeden, editors,
Developmental Robotics 2005 AAAI Spring Symposium,
March 21-23, 2005, Stanford University, CA.
PDF.
90.
M. Gagliolo, V. Zhumatiy and J. Schmidhuber.
Adaptive Online Time Allocation to Search Algorithms.
In J. F. Boulicaut et al., eds., Proceedings of the
15th European Conference on Machine Learning ECML, Pisa, Italy,
September 20-24, Springer, 2004.
89.
Schmidhuber, J., Zhumatiy, V. and Gagliolo, M. Bias-Optimal
Incremental Learning of Control Sequences for Virtual Robots. In Groen,
F., Amato, N., Bonarini, A., Yoshida, E., and Kröse, B., editors:
Proceedings of the 8-th conference
on Intelligent Autonomous Systems, IAS-8, Amsterdam,
The Netherlands, pp. 658-665, 2004.
PDF.
88.
A. Graves, N. Beringer, J. Schmidhuber.
A Comparison Between Spiking and Differentiable Recurrent
Neural Networks on Spoken Digit Recognition.
In Proc. 23rd International Conference on modelling, identification,
and control (IASTED), 2004.
PDF.
87.
B. Bakker and J. Schmidhuber.
Hierarchical Reinforcement
Learning Based on Subgoal Discovery and Subpolicy Specialization
(PDF).
In F. Groen, N. Amato, A. Bonarini, E. Yoshida, and B. Kröse (Eds.),
Proceedings of the 8-th Conference on Intelligent Autonomous Systems,
IAS-8, Amsterdam, The Netherlands, p. 438-445, 2004.
86.
A. Graves, D. Eck and N. Beringer, J. Schmidhuber.
Biologically Plausible Speech Recognition with LSTM Neural Nets.
In J. Ijspeert (Ed.),
First Intl. Workshop on Biologically
Inspired Approaches to Advanced Information Technology,
Bio-ADIT 2004, Lausanne, Switzerland, p. 175-184, 2004.
PDF .
85.
B. Bakker and J. Schmidhuber.
Hierarchical Reinforcement
Learning with Subpolicies Specializing for Learned Subgoals. In
Proceedings of the 2nd IASTED International Conference on Neural Networks
and Computational Intelligence, NCI 2004, Grindelwald,
Switzerland, 2004.
PDF.
84.
J. Schmidhuber.
Bias-Optimal Incremental Problem Solving.
In S. Becker, S. Thrun, K. Obermayer, eds.,
Advances in Neural Information Processing Systems 15, NIPS'15,
MIT Press, Cambridge MA, p. 1571-1578, 2003.
PDF .
HTML.
(Compact version of
Optimal Ordered Problem Solver.
)
83.
B. Bakker, V. Zhumatiy, G. Gruener, and J. Schmidhuber.
A Robot that Reinforcement-Learns to Identify and Memorize Important
Previous Observations
(PDF).
In Proceedings of the 2003 IEEE/RSJ
International Conference on Intelligent Robots and Systems, IROS2003, 2003.
82.
Bakker, B., and Schmidhuber, J. (2003).
Hierarchical Reinforcement Learning Based on Automatic
Discovery of Subgoals and Specialization of Subpolicies.
In Proceedings of the 2003 European Workshop on
Reinforcement Learning, EWRL 6, Nancy, France.
81.
J. Schmidhuber.
The Speed Prior: A New Simplicity Measure
Yielding Near-Optimal Computable Predictions.
In J. Kivinen and R. H. Sloan, editors, Proceedings of the 15th
Annual Conference on Computational Learning Theory (COLT 2002), Sydney, Australia,
Lecture Notes in Artificial Intelligence, pages 216--228. Springer, 2002.
PDF .
HTML.
80.
B. Bakker, F. Linaker, J. Schmidhuber.
Reinforcement Learning in Partially Observable Mobile Robot
Domains Using Unsupervised Event Extraction.
In Proceedings of the 2002
IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2002), Lausanne, 2002.
PDF .
79.
J. Schmidhuber.
Recent Progress in the Fields
of Universal Learning Algorithms
and Optimal Search.
In Proceedings of EUNITE 2002, p. 11-20,
Albufeira, Portugal, 2002 (invited talk).
78.
J. Schmidhuber.
Speed Prior and Optimal Simulation of the Future.
In M. Ades and L. M. Deschaine, editors,
Proceedings of the Business and Industry Symposium, 2002 Advanced
Simulation Technologies Conference,
San Diego, California.
Simulation Series, vol. 34:4, p. 40-45, 2002 (invited).
77.
D. Eck and J. Schmidhuber.
Finding temporal structure in music:
Blues improvisation with LSTM recurrent networks.
In S. Bengio, editor, Proc. NNSP 2002, IEEE, 2002.
PDF.
76.
D. Eck and J. Schmidhuber.
Learning The Long-Term Structure of the Blues.
In J. Dorronsoro, ed.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'02, Madrid, pages 284-289, Springer, Berlin, 2002.
PDF.
75.
F. Gers and J. A. Perez-Ortiz and D. Eck and J. Schmidhuber.
Learning Context Sensitive Languages with LSTM Trained with Kalman Filters.
In J. Dorronsoro, ed.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'02, Madrid, pages 655--660, Springer, Berlin, 2002.
PDF.
74.
F. A. Gers, J. A. Pérez-Ortiz, D. Eck, and J. Schmidhuber.
DEKF-LSTM.
In Verleysen, editor, 10th European Symposium
on Artificial Neural Networks. ESANN'2002. Proceedings.
Brussels, Belgium, pages 369-376, 2002.
PDF.
73.
J. A. Perez-Ortiz, J. Schmidhuber, F. Gers and D. Eck.
Improving Long-Term Online
Prediction with Decoupled Extended Kalman Filters.
In J. Dorronsoro, ed.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'02, Madrid, pages 1055--1060, Springer, Berlin, 2002.
PDF.
72.
I. Kwee, M. Hutter, J. Schmidhuber.
Market-Based Reinforcement Learning in Partially Observable Worlds.
In G. Dorffner, H. Bischof, K. Hornik, eds.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'01, Vienna, LNCS 2130, pages 865-873, Springer, 2001.
PDF.
71.
F. Gers, D. Eck, J . Schmidhuber.
Applying LSTM to Time Series Predictable Through Time-Window Approaches.
In G. Dorffner, H. Bischof, K. Hornik, eds.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'01, Vienna, LNCS 2130, pages 669-676, Springer, 2001.
PDF.
70.
M. Klapper-Rybicka, N. N. Schraudolph, J. Schmidhuber.
Unsupervised Learning in LSTM Recurrent Neural Networks.
In G. Dorffner, H. Bischof, K. Hornik, eds.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'01, Vienna, LNCS 2130, pages 684-691, Springer, 2001.
PDF.
69.
F. A. Gers and J. Schmidhuber.
Long Short-Term Memory learns context free and
context sensitive languages.
In Kurkova et. al., editors, Proceedings
of the ICANNGA 2001 Conference, volume 1,
pages 134-137, Wien,NY, 2001. Springer.
PDF.
68.
M. Milano, J. Schmidhuber, P. Koumoutsakos.
Active Learning with Adaptive Grids.
In G. Dorffner, H. Bischof, K. Hornik, eds.,
Proceedings of Int. Conf. on Artificial Neural Networks
ICANN'01, Vienna, LNCS 2130, pages 436-442, Springer, 2001.
PDF.
67.
J. Schmidhuber.
Evolutionary Computation vs Reinforcement Learning.
Proceedings of 3rd Asia-Pacific Conference on Simulated
Evolution and Learning (SEAL2000), Nagoya, Japan, October 2000.
PDF.
(Keynote speech)
66.
I. W. Kwee and J. Schmidhuber.
Direct policy computation by the Liouville Machine.
Proceedings of SOAVE 2000, Ilmenau (Germany),
2000.
PDF.
65.
F. A. Gers and J. Schmidhuber.
Neural processing of complex continual input streams.
In Proc. IJCNN'2000, Int. Joint Conf. on Neural Networks,
Como, Italy, 2000.
PDF.
64.
F. A. Gers and J. Schmidhuber.
Recurrent nets that time and count.
In Proc. IJCNN'2000, Int. Joint Conf. on Neural Networks, Como, Italy, 2000.
PDF.
63.
M. Milano, X. Giannakopoulos, P. Koumoutsakos, and J. Schmidhuber.
Evolving strategies for active flow control.
Congress on Evolutionary Computation, USA, July 2000.
PDF.
62.
F. A. Gers and J. Schmidhuber and F. Cummins.
Learning to Forget: Continual Prediction with LSTM.
In Proc. Int. Conf. on Artificial Neural Networks (ICANN'99),
Edinburgh, Scotland, p. 850-855, IEE, London, 1999.
61.
J . Schmidhuber.
Artificial Curiosity Based on Discovering Novel Algorithmic
Predictability Through Coevolution.
In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, Z.
Zalzala, eds., Congress on Evolutionary Computation, p. 1612-1618,
IEEE Press, Piscataway, NJ, 1999.
60.
F. Cummins, F. Gers, J . Schmidhuber.
Language identification from prosody without explicit features.
Proceedings of EUROSPEECH99, 1999.
59.
J. Schmidhuber. Contribution to
A. A. Frolov and A. A. Ezhof, eds.,
Discussion on neurocomputers after ten years,
Moscow Institute of Engineering and Physics, January 1999,
published in
Neural Network World 1-2, 112-113, 1999.
58.
J. Schmidhuber and J. Zhao.
Direct policy search and uncertain policy evaluation.
1999 AAAI Spring Symposium on Search
under Uncertain and Incomplete Information,
119-124, Stanford Univ., 1999.
Based on TR IDSIA-50-98, 1998.
57.
J. Schmidhuber.
Direct policy evaluation in stochastic environments with unknown delays.
In Abstract Collection of SNOWBIRD:
Machines That Learn.
Utah, April 1999.
56.
S. Hochreiter and J. Schmidhuber.
Nonlinear ICA through low-complexity autoencoders.
Proceedings of the 1999 IEEE International Symposium on Circuits ans Systems
(ISCAS'99), vol 5, p. 53-56, Orlando, Florida, 1999.
55.
S. Hochreiter and J. Schmidhuber.
Source separation as a by-product of regularization.
In M. S. Kearns, S. A. Solla, D. A. Cohn, eds.,
Advances in Neural Information Processing Systems 11, NIPS'11,
p. 459-465, MIT Press, Cambridge MA, 1999.
PDF .
HTML.
54.
S. Hochreiter and J. Schmidhuber.
LOCOCODE performs nonlinear ICA without knowing the
number of sources.
In J.-F. Cardoso and C. Jutten and P. Loubaton, eds.,
Proceedings of the First International Workshop on
Independent Component Analysis and Signal Separation
(ICA'99), 149-154, Aussois, France, 1999.
53.
J. Schmidhuber.
What's interesting?
In Abstract Collection of SNOWBIRD:
Machines That Learn.
Utah, April 1998 (based on TR IDSIA-35-97, 1997).
52.
R. Salustowicz and J. Schmidhuber.
Evolving structured programs with hierarchical
instructions and skip nodes.
In Jude Shavlik, ed.,
Machine Learning:
Proceedings of the 15th International Conference (ICML 1998),
p. 488-496,
Morgan Kaufmann Publishers, San Francisco, CA,
1998.
51.
S. Hochreiter and J. Schmidhuber.
LOCOCODE versus PCA and ICA.
In L. Niklasson and M. Boden and T. Ziemke, eds.,
Proceedings of the International Conference on
Artificial Neural Networks, Sweden,
p. 669-674,
Springer, London, 1998.
50.
M. Wiering and J. Schmidhuber.
CMAC Models Learn to Play Soccer.
In L. Niklasson and M. Boden and T. Ziemke, eds.,
Proceedings of the International Conference on
Artificial Neural Networks, Sweden,
p. 443-448, Springer, London, 1998.
49.
M. Wiering and J. Schmidhuber.
Learning exploration policies with models.
In Proc. CONALD, 1998.
48.
M. Wiering and J. Schmidhuber.
Efficient model-based exploration.
In R. Pfeiffer, B. Blumberg, J. Meyer, S. W. Wilson, eds.,
From Animals to Animats 5: Proceedings
of the Fifth International Conference on Simulation of Adaptive
Behavior, p. 223-228, MIT Press, 1998.
47.
J. Zhao and J. Schmidhuber.
Solving a complex prisoner's dilemma
with self-modifying policies.
In R. Pfeiffer, B. Blumberg, J. Meyer, S. W. Wilson, eds.,
From Animals to Animats 5: Proceedings
of the Fifth International Conference on Simulation of Adaptive
Behavior, p177-182, MIT Press, 1998.
46.
M. Wiering and J. Schmidhuber.
Speeding up online Q(lambda)-learning.
In Proc. Machine Learning: ECML-98,
Lecture Notes in Artificial Intelligence,
Springer, 1998.
45.
S. Hochreiter and J. Schmidhuber.
LSTM can solve hard long time lag problems.
In M. C. Mozer, M. I. Jordan, T. Petsche, eds.,
Advances in Neural Information Processing Systems 9, NIPS'9,
pages 473-479, MIT Press, Cambridge MA, 1997.
PDF .
HTML.
44.
R. Salustowicz and M. Wiering and J. Schmidhuber.
Evolving soccer strategies.
In N. Kasabov, R. Kozma, K. Ko, R. O'Shea, G. Coghill, and T. Gedeon, editors,
Progress in Connectionist-based Information Systems: Proceedings of the Fourth
International Conference on Neural Information Processing ICONIP'97, volume 1,
pages 502-505, 1997.
43.
S. Hochreiter and J. Schmidhuber.
Low-complexity coding and decoding.
In K. M. Wong, I. King, D. Yeung, eds.,
Theoretical Aspects of Neural Computation: a Multidisciplinary Perspective,
pages 297-306, Springer, 1997.
42.
J. Schmidhuber and J. Zhao.
Multiagent learning with the success-story algorithm.
In G. Weiss, ed.,
Distributed Artificial Intelligence
Meets Machine Learning, pages 82-93,
Springer, Berlin, 1997.
41.
R. Salustowicz and M. Wiering and J. Schmidhuber.
On learning soccer strategies.
In W. Gerstner, A. Germond, M. Hasler, J.-D. Nicoud, eds.,
Proceedings of the International Conference on
Artificial Neural Networks, Lausanne, Switzerland,
Springer, 769-774, 1997.
40.
S. Hochreiter and J. Schmidhuber.
Unsupervised coding with LOCOCODE.
In W. Gerstner, A. Germond, M. Hasler, J.-D. Nicoud, eds.,
Proceedings of the International Conference on
Artificial Neural Networks, Lausanne, Switzerland,
Springer, 655-660, 1997.
39.
R. Salustowicz and J. Schmidhuber.
Probabilistic incremental program evolution:
stochastic search through program space.
In van Someren, M.,
Widmer, G., editors, Machine Learning: ECML-97,
Lecture Notes in Artificial Intelligence 1224,
pages 213-220, Springer, 1997.
38.
M. Wiering and J. Schmidhuber.
Solving POMDPs using Levin search and EIRA.
In L. Saitta, ed.,
Machine Learning:
Proceedings of the 13th International Conference (ICML 1996),
pages 534-542,
Morgan Kaufmann Publishers, San Francisco, CA, 1996.
PDF .
HTML.
37.
J. Zhao and J. Schmidhuber.
Incremental self-improvement for
life-time multiagent reinforcement learning.
In Pattie Maes, Maja Mataric, Jean-Arcady Meyer, Jordan Pollack,
and Stewart W. Wilson, eds.,
From Animals to Animats 4: Proceedings
of the Fourth International Conference on Simulation of Adaptive
Behavior, pages 516-525, MIT Press, Bradford Books, Cambridge, MA, 1996.
36.
J. Schmidhuber.
The Neural Heat Exchanger.
In S. Amari, L. Xu, L. Chan, I. King, K. Leung, eds.,
Progress in Neural Information
Processing: Proceedings of the Intl. Conference
on Neural Information Processing, pages 194-197,
Springer, Hongkong, 1996. Earlier presentations
in talks at various universities since 1990.
PDF .
HTML.
35.
S. Hochreiter and J. Schmidhuber.
Bridging long time lags by weight guessing and ``Long Short-Term Memory''.
In F. L. Silva, J. C. Principe, L. B. Almeida, eds.,
Frontiers in Artificial Intelligence and Applications, Volume 37,
pages 65-72, IOS Press, Amsterdam, Netherlands, 1996.
34.
J. Schmidhuber.
Realistic multiagent reinforcement learning.
In G. Weiss, ed., Learning in Distributed
Artificial Intelligence Systems. Working Notes of the
1996 ECAI Workshop, 1996.
33.
J. Schmidhuber.
A general method for multiagent learning
in unrestricted environments.
In 1996 AAAI Syposium on Adaptation, Co-evolution and
Learning in Multiagent Systems, TR SS-96-01,
pages 84-87, AAAI Press, Menlo Park, Calif., 1996.
32.
S. Hochreiter and J. Schmidhuber.
Simplifying neural nets by discovering flat minima.
In G. Tesauro, D. S. Touretzky and T. K. Leen, eds.,
Advances in Neural Information Processing Systems 7, NIPS'7,
pages 529-536.
MIT Press, Cambridge MA, 1995.
PDF .
HTML.
31.
J. Schmidhuber and S. Heil.
Predictive coding with neural nets: Application to text compression.
In G. Tesauro, D. S. Touretzky and T. K. Leen, eds.,
Advances in Neural Information Processing Systems 7, NIPS'7, pages 1047-1054.
MIT Press, Cambridge MA, 1995.
PDF .
HTML.
30.
J. Schmidhuber.
Discovering solutions with low Kolmogorov complexity
and high generalization capability.
In A. Prieditis and S. Russell, editors, Machine Learning:
Proceedings of the Twelfth International Conference (ICML 1995),
pages 488-496. Morgan
Kaufmann Publishers, San Francisco, CA, 1995.
PDF .
HTML.
29.
J. Schmidhuber.
Beyond ``Genetic Programming'': Incremental Self-Improvement.
In J. Rosca, ed., Proc. Workshop on Genetic Programming at ML95,
pages 42-49. National Resource Lab for the study of Brain and Behavior,
1995.
28.
J. Storck, S. Hochreiter, and J. Schmidhuber.
Reinforcement-driven information acquisition in non-deterministic
environments.
In Proc. ICANN'95, vol. 2, pages 159-164.
EC2 & CIE, Paris, 1995.
PDF.
HTML.
27.
J. Schmidhuber.
A neural network that embeds its own meta-levels.
In Proc. of the International Conference on Neural Networks '93,
San Francisco. IEEE, 1993.
26.
J. Schmidhuber.
``Neural'' redundancy reduction for text compression.
In Neural Network World , 3(6):849-853, 1993.
25.
J. Schmidhuber.
An introspective network that can learn to run its own weight change algorithm.
In Proc. of the Intl. Conf. on Artificial Neural Networks,
Brighton, pages 191-195. IEE, 1993.
24.
J. Schmidhuber.
A self-referential weight matrix.
In Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam, pages 446-451. Springer, 1993.
PDF .
HTML.
23.
J. Schmidhuber.
Reducing the ratio between learning complexity and number of
time-varying variables in fully recurrent nets.
In Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam, pages 460-463. Springer, 1993.
PDF.
HTML.
22.
J. Schmidhuber and D. Prelinger.
Unsupervised extraction of predictable abstract features.
In Proceedings of the International Conference on Artificial
Neural Networks, Amsterdam, pages 601-604. Springer, 1993.
21.
J. Schmidhuber and D. Prelinger.
A novel unsupervised classification method.
In Proc. of the Intl. Conf. on Artificial Neural Networks,
Brighton, pages 91-96. IEE, 1993.
20.
J. Schmidhuber, M. C. Mozer, and D. Prelinger.
Continuous history compression.
In H. Hüning, S. Neuhauser, M. Raus, and W. Ritschel, editors,
Proc. of Intl. Workshop on Neural Networks, RWTH Aachen, pages 87-95.
Augustinus, 1993.
19.
J. Schmidhuber and R. Wahnsiedler.
Planning simple trajectories using neural subgoal generators.
In J. A. Meyer, H. L. Roitblat, and S. W. Wilson, editors, Proc.
of the 2nd International Conference on Simulation of Adaptive Behavior,
pages 196-202. MIT Press, 1992.
PDF .
HTML without images.
HTML & images in German.
18.
J. Schmidhuber.
Learning unambiguous reduced sequence descriptions.
In J. E. Moody, S. J. Hanson, and R. P. Lippman, editors,
Advances in Neural Information Processing Systems 4, NIPS'4, pages 291-298. San
Mateo, CA: Morgan Kaufmann, 1992.
PDF .
HTML.
17.
J. Schmidhuber.
Reinforcement learning in Markovian and non-Markovian environments.
In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors,
Advances in Neural Information Processing Systems 3, NIPS'3, pages 500-506. San
Mateo, CA: Morgan Kaufmann, 1991.
PDF .
HTML.
16.
J. Schmidhuber.
Learning temporary variable binding with dynamic links.
In Proc. International Joint Conference on Neural Networks,
Singapore, volume 3, pages 2075-2079. IEEE, 1991.
15.
J. Schmidhuber.
Curious model-building control systems.
In Proc. International Joint Conference on Neural Networks,
Singapore, volume 2, pages 1458-1463. IEEE, 1991.
PDF .
HTML.
14.
J. Schmidhuber.
Adaptive history compression for learning to divide and conquer.
In Proc. International Joint Conference on Neural Networks,
Singapore, volume 2, pages 1130-1135. IEEE, 1991.
13.
J. Schmidhuber.
Learning to generate sub-goals for action sequences.
In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors,
Artificial Neural Networks, pages 967-972. Elsevier Science Publishers
B.V., North-Holland, 1991.
PDF .
HTML.
HTML & images in German.
12.
J. Schmidhuber.
Adaptive decomposition of time.
In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors,
Artificial Neural Networks, pages 909-914. Elsevier Science Publishers
B.V., North-Holland, 1991.
11.
J. Schmidhuber and R. Huber.
Using sequential adaptive neuro-control for efficient learning of
rotation and translation invariance.
In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors,
Artificial Neural Networks, pages 315-320. Elsevier Science Publishers
B.V., North-Holland, 1991.
10.
J. Schmidhuber.
A possibility for implementing curiosity and boredom in
model-building neural controllers.
In J. A. Meyer and S. W. Wilson, editors, Proc. of the
International Conference on Simulation of Adaptive Behavior: From Animals to
Animats, pages 222-227. MIT Press/Bradford Books, 1991.
PDF .
HTML.
9.
J. Schmidhuber.
Learning algorithms for networks with internal and external feedback.
In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton,
editors, Proc. of the 1990 Connectionist Models Summer School, pages
52-61. San Mateo, CA: Morgan Kaufmann, 1990. PS. (PDF.)
8.
J. Schmidhuber.
An on-line algorithm for dynamic reinforcement learning and planning
in reactive environments.
In Proc. IEEE/INNS International Joint Conference on Neural
Networks, San Diego, volume 2, pages 253-258, 1990.
7.
J. Schmidhuber.
Reinforcement learning with interacting continually running fully
recurrent networks.
In Proc. INNC International Neural Network Conference, Paris,
volume 2, pages 817-820, 1990.
6.
J. Schmidhuber.
Temporal difference-driven learning in recurrent networks.
In R. Eckmiller, G. Hartmann, and G. Hauske, editors, Parallel
Processing in Neural Systems and Computers, pages 209-212. North-Holland,
1990.
5.
J. Schmidhuber.
Reinforcement-Lernen und adaptive Steuerung.
Nachrichten Neuronale Netze, 2:1-3, 1990.
4.
J. Schmidhuber.
Recurrent networks adjusted by adaptive critics.
In Proc. IEEE/INNS International Joint Conference on Neural
Networks, Washington, D. C., volume 1, pages 719-722, 1990.
3.
J. Schmidhuber.
Networks adjusting networks.
In J. Kindermann and A. Linden, editors, Proceedings of
`Distributed Adaptive Neural Information Processing', St.Augustin, 24.-25.5.
1989, pages 197-208. Oldenbourg, 1990.
Extended version: TR FKI-125-90 (revised),
Institut für Informatik, TUM.
PDF.
2.
J. Schmidhuber.
The neural bucket brigade.
In R. Pfeifer, Z. Schreter, Z. Fogelman, and L. Steels, editors,
Connectionism in Perspective, pages 439-446. Amsterdam: Elsevier,
North-Holland, 1989. See TR FKI-124-90:
PDF.
1.
J. Schmidhuber.
Accelerated learning in back-propagation nets.
In R. Pfeifer, Z. Schreter, Z. Fogelman, and L. Steels, editors,
Connectionism in Perspective, pages 429 - 438. Amsterdam: Elsevier,
North-Holland, 1989.