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ORCIDJürgen Schmidhuber
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- affiliation: University of Applied Sciences and Arts of Southern Switzerland, Switzerland
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2020 – today
- 2021
[i98]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Memory Systems. CoRR abs/2102.11174 (2021)- [i97]Djordje Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. CoRR abs/2103.08877 (2021) - [i96]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it Enough to Optimize CNN Architectures on ImageNet? CoRR abs/2103.09108 (2021) - 2020
- [j69]Jürgen Schmidhuber
:
Generative Adversarial Networks are special cases of Artificial Curiosity (1990) and also closely related to Predictability Minimization (1991). Neural Networks 127: 58-66 (2020) - [j68]Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly:
Investigating object compositionality in Generative Adversarial Networks. Neural Networks 130: 309-325 (2020) - [c209]Matteo Riva, Michael Wand, Jürgen Schmidhuber:
Motion Dynamics Improve Speaker-Independent Lipreading. ICASSP 2020: 4407-4411 - [c208]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR 2020 - [c207]Michael Wand, Jürgen Schmidhuber:
Fusion Architectures for Word-Based Audiovisual Speech Recognition. INTERSPEECH 2020: 3491-3495 - [i95]Jürgen Schmidhuber:
Deep Learning: Our Miraculous Year 1990-1991. CoRR abs/2005.05744 (2020) - [i94]Francesco Faccio, Jürgen Schmidhuber:
Parameter-based Value Functions. CoRR abs/2006.09226 (2020) - [i93]Aditya Ramesh, Paulo E. Rauber, Jürgen Schmidhuber:
Recurrent Neural-Linear Posterior Sampling for Non-Stationary Contextual Bandits. CoRR abs/2007.04750 (2020) - [i92]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. CoRR abs/2010.02066 (2020) - [i91]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. CoRR abs/2010.03635 (2020) - [i90]Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber:
Learning Associative Inference Using Fast Weight Memory. CoRR abs/2011.07831 (2020) - [i89]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. CoRR abs/2011.12930 (2020) - [i88]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
On the Binding Problem in Artificial Neural Networks. CoRR abs/2012.05208 (2020) - [i87]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. CoRR abs/2012.14905 (2020)
2010 – 2019
- 2019
- [c206]Krsto Prorokovic, Michael Wand, Tanja Schultz, Jürgen Schmidhuber:
Adaptation of an EMG-Based Speech Recognizer via Meta-Learning. GlobalSIP 2019: 1-5 - [c205]Róbert Csordás, Jürgen Schmidhuber:
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. ICLR (Poster) 2019 - [c204]Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. ICLR (Poster) 2019 - [c203]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? NeurIPS 2019: 14222-14235 - [i86]Róbert Csordás, Jürgen Schmidhuber:
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. CoRR abs/1904.10278 (2019) - [i85]Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem:
Are Disentangled Representations Helpful for Abstract Visual Reasoning? CoRR abs/1905.12506 (2019) - [i84]Sjoerd van Steenkiste, Klaus Greff, Jürgen Schmidhuber:
A Perspective on Objects and Systematic Generalization in Model-Based RL. CoRR abs/1906.01035 (2019) - [i83]Jürgen Schmidhuber:
Unsupervised Minimax: Adversarial Curiosity, Generative Adversarial Networks, and Predictability Minimization. CoRR abs/1906.04493 (2019) - [i82]Timon Willi, Jonathan Masci, Jürgen Schmidhuber, Christian Osendorfer:
Recurrent Neural Processes. CoRR abs/1906.05915 (2019) - [i81]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. CoRR abs/1910.04098 (2019) - [i80]Aleksandar Stanic, Jürgen Schmidhuber:
R-SQAIR: Relational Sequential Attend, Infer, Repeat. CoRR abs/1910.05231 (2019) - [i79]Imanol Schlag, Paul Smolensky, Roland Fernandez, Nebojsa Jojic, Jürgen Schmidhuber, Jianfeng Gao:
Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving. CoRR abs/1910.06611 (2019) - [i78]Jürgen Schmidhuber:
Reinforcement Learning Upside Down: Don't Predict Rewards - Just Map Them to Actions. CoRR abs/1912.02875 (2019) - [i77]Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaskowski, Jürgen Schmidhuber:
Training Agents using Upside-Down Reinforcement Learning. CoRR abs/1912.02877 (2019) - 2018
- [c202]Michael Wand, Jürgen Schmidhuber, Ngoc Thang Vu:
Investigations on End- to-End Audiovisual Fusion. ICASSP 2018: 3041-3045 - [c201]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. ICLR (Poster) 2018 - [c200]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects. ICPR 2018: 3704-3709 - [c199]Michael Wand, Tanja Schultz, Jürgen Schmidhuber:
Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. INTERSPEECH 2018: 3167-3171 - [c198]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. ISMIR 2018: 271-278 - [c197]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. NeurIPS 2018: 2455-2467 - [c196]Imanol Schlag, Jürgen Schmidhuber:
Learning to Reason with Third Order Tensor Products. NeurIPS 2018: 10003-10014 - [i76]Jürgen Schmidhuber:
One Big Net For Everything. CoRR abs/1802.08864 (2018) - [i75]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. CoRR abs/1802.10353 (2018) - [i74]David Ha, Jürgen Schmidhuber:
World Models. CoRR abs/1803.10122 (2018) - [i73]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Marcello Pelillo, Thilo Stadelmann:
DeepScores - A Dataset for Segmentation, Detection and Classification of Tiny Objects. CoRR abs/1804.00525 (2018) - [i72]Michael Wand, Ngoc Thang Vu, Jürgen Schmidhuber:
Investigations on End-to-End Audiovisual Fusion. CoRR abs/1804.11127 (2018) - [i71]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. CoRR abs/1805.10548 (2018) - [i70]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. CoRR abs/1809.01999 (2018) - [i69]Imanol Schlag, Jürgen Schmidhuber:
Learning to Reason with Third-Order Tensor Products. CoRR abs/1811.12143 (2018) - 2017
- [j67]Varun Raj Kompella, Marijn F. Stollenga, Matthew D. Luciw, Jürgen Schmidhuber:
Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots. Artif. Intell. 247: 313-335 (2017) - [j66]Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber:
LSTM: A Search Space Odyssey. IEEE Trans. Neural Networks Learn. Syst. 28(10): 2222-2232 (2017) - [c195]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. ICLR (Poster) 2017 - [c194]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. ICLR (Workshop) 2017 - [c193]Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. ICML 2017: 4189-4198 - [c192]Michael Wand, Jürgen Schmidhuber:
Improving Speaker-Independent Lipreading with Domain-Adversarial Training. INTERSPEECH 2017: 3662-3666 - [c191]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. NIPS 2017: 6691-6701 - [r1]Jürgen Schmidhuber:
Deep Learning. Encyclopedia of Machine Learning and Data Mining 2017: 338-348 - [i68]Michael Wand, Jürgen Schmidhuber:
Improving Speaker-Independent Lipreading with Domain-Adversarial Training. CoRR abs/1708.01565 (2017) - [i67]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. CoRR abs/1708.03498 (2017) - [i66]Paulo E. Rauber, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. CoRR abs/1711.06006 (2017) - 2016
- [j65]Varun Raj Kompella, Matthew D. Luciw, Marijn F. Stollenga, Jürgen Schmidhuber:
Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis. Neural Comput. 28(8): 1599-1662 (2016) - [j64]Alessandro Giusti, Jerome Guzzi, Dan C. Ciresan, Fang-Lin He, Juan P. Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jürgen Schmidhuber, Gianni Di Caro, Davide Scaramuzza, Luca Maria Gambardella:
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots. IEEE Robotics Autom. Lett. 1(2): 661-667 (2016) - [c190]Bas R. Steunebrink, Kristinn R. Thórisson, Jürgen Schmidhuber:
Growing Recursive Self-Improvers. AGI 2016: 129-139 - [c189]Sjoerd van Steenkiste, Jan Koutník, Kurt Driessens, Jürgen Schmidhuber:
A Wavelet-based Encoding for Neuroevolution. GECCO 2016: 517-524 - [c188]Michael Wand, Jan Koutník, Jürgen Schmidhuber:
Lipreading with long short-term memory. ICASSP 2016: 6115-6119 - [c187]Michael Wand, Jürgen Schmidhuber:
Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition. INTERSPEECH 2016: 3032-3036 - [c186]Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Harri Valpola, Jürgen Schmidhuber:
Tagger: Deep Unsupervised Perceptual Grouping. NIPS 2016: 4484-4492 - [i65]Michael Wand, Jan Koutník, Jürgen Schmidhuber:
Lipreading with Long Short-Term Memory. CoRR abs/1601.08188 (2016) - [i64]Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, Harri Valpola:
Tagger: Deep Unsupervised Perceptual Grouping. CoRR abs/1606.06724 (2016) - [i63]Simon Harding, Jan Koutník, Klaus Greff, Jürgen Schmidhuber, Andy Adamatzky:
Discovering Boolean Gates in Slime Mould. CoRR abs/1607.02168 (2016) - [i62]Julian G. Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. CoRR abs/1607.03474 (2016) - [i61]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. CoRR abs/1612.07771 (2016) - 2015
- [j63]Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio A. González, Anders Boesen Lindbo Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Ciresan, Jürgen Schmidhuber, Alessandro Giusti, Luca Maria Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frédéric Precioso, Violet Snell, Josef Kittler, Teófilo Emídio de Campos, Adnan Mujahid Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim:
Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Anal. 20(1): 237-248 (2015) - [j62]Jürgen Schmidhuber:
Deep learning in neural networks: An overview. Neural Networks 61: 85-117 (2015) - [j61]Jürgen Schmidhuber:
Deep Learning. Scholarpedia 10(11): 32832 (2015) - [c185]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Jürgen Schmidhuber:
Anytime Bounded Rationality. AGI 2015: 121-130 - [c184]Marijn F. Stollenga, Alan J. Lockett, Jürgen Schmidhuber:
The Natural Gradient as a control signal for a humanoid robot. Humanoids 2015: 187-193 - [c183]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. NIPS 2015: 2377-2385 - [c182]Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber:
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. NIPS 2015: 2998-3006 - [c181]Rupesh Kumar Srivastava, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Understanding Locally Competitive Networks. ICLR (Poster) 2015 - [i60]Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník, Bas R. Steunebrink, Jürgen Schmidhuber:
LSTM: A Search Space Odyssey. CoRR abs/1503.04069 (2015) - [i59]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Highway Networks. CoRR abs/1505.00387 (2015) - [i58]Marijn F. Stollenga, Wonmin Byeon, Marcus Liwicki, Jürgen Schmidhuber:
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. CoRR abs/1506.07452 (2015) - [i57]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. CoRR abs/1507.06228 (2015) - [i56]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Binding via Reconstruction Clustering. CoRR abs/1511.06418 (2015) - [i55]Jürgen Schmidhuber:
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. CoRR abs/1511.09249 (2015) - 2014
- [j60]Daan Wierstra, Tom Schaul, Tobias Glasmachers, Yi Sun, Jan Peters, Jürgen Schmidhuber:
Natural evolution strategies. J. Mach. Learn. Res. 15(1): 949-980 (2014) - [j59]Jonathan Masci, Michael M. Bronstein, Alexander M. Bronstein, Jürgen Schmidhuber:
Multimodal Similarity-Preserving Hashing. IEEE Trans. Pattern Anal. Mach. Intell. 36(4): 824-830 (2014) - [j58]Hung Quoc Ngo, Matthew D. Luciw, Jawad Nagi, Alexander Förster, Jürgen Schmidhuber, Ngo Anh Vien:
Efficient Interactive Multiclass Learning from Binary Feedback. ACM Trans. Interact. Intell. Syst. 4(3): 12:1-12:25 (2014) - [c180]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Haris Dindo, Giovanni Pezzulo, Manuel Rodríguez, Carlos Hernández, Dimitri Ognibene, Jürgen Schmidhuber, Ricardo Sanz, Helgi Páll Helgason, Antonio Chella:
Bounded Seed-AGI. AGI 2014: 85-96 - [c179]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. GECCO 2014: 541-548 - [c178]Jawad Nagi, Hung Quoc Ngo, Jürgen Schmidhuber, Luca Maria Gambardella, Gianni A. Di Caro:
Human-robot cooperation: fast, interactive learning from binary feedback. HRI 2014: 107 - [c177]Matthew D. Luciw, Yulia Sandamirskaya, Sohrob Kazerounian, Jürgen Schmidhuber, Gregor Schöner:
Reinforcement and shaping in learning action sequences with neural dynamics. ICDL-EPIROB 2014: 48-55 - [c176]Jürgen Leitner, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Reactive Reaching and Grasping on a Humanoid - Towards Closing the Action-Perception Loop on the iCub. ICINCO (1) 2014: 102-109 - [c175]Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. ICML 2014: 1863-1871 - [c174]Varun Raj Kompella, Marijn F. Stollenga, Matthew D. Luciw, Jürgen Schmidhuber:
Explore to see, learn to perceive, get the actions for free: SKILLABILITY. IJCNN 2014: 2705-2712 - [c173]Jürgen Leitner, Alexander Förster, Jürgen Schmidhuber:
Improving robot vision models for object detection through interaction. IJCNN 2014: 3355-3362 - [c172]Alessandro Giusti, Claudio Caccia, Dan C. Ciresan, Jürgen Schmidhuber, Luca Maria Gambardella:
A comparison of algorithms and humans for mitosis detection. ISBI 2014: 1360-1363 - [c171]Jan Funke, Julien N. P. Martel, Stephan Gerhard, Björn Andres, Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber, Hanspeter Pfister, Albert Cardona, Matthew Cook:
Candidate Sampling for Neuron Reconstruction from Anisotropic Electron Microscopy Volumes. MICCAI (1) 2014: 17-24 - [c170]Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Deep Networks with Internal Selective Attention through Feedback Connections. NIPS 2014: 3545-3553 - [c169]Varun Raj Kompella, Sohrob Kazerounian, Jürgen Schmidhuber:
An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning. SAB 2014: 176-187 - [c168]Matthew D. Luciw, Sohrob Kazerounian, Yulia Sandamirskaya, Gregor Schöner, Jürgen Schmidhuber:
Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics. SAB 2014: 198-209 - [c167]Marijn F. Stollenga, Jürgen Schmidhuber, Faustino J. Gomez:
Rapid Humanoid Motion Learning through Coordinated, Parallel Evolution. SAB 2014: 210-219 - [c166]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
Online Evolution of Deep Convolutional Network for Vision-Based Reinforcement Learning. SAB 2014: 260-269 - [p7]Jonathan Masci, Davide Migliore, Michael M. Bronstein, Jürgen Schmidhuber:
Descriptor Learning for Omnidirectional Image Matching. Registration and Recognition in Images and Videos 2014: 49-62 - [i54]Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. CoRR abs/1402.3511 (2014) - [i53]Jürgen Schmidhuber:
Deep Learning in Neural Networks: An Overview. CoRR abs/1404.7828 (2014) - [i52]Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, Jürgen Schmidhuber:
Deep Networks with Internal Selective Attention through Feedback Connections. CoRR abs/1407.3068 (2014) - [i51]Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio A. González, Anders Boesen Lindbo Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Ciresan, Jürgen Schmidhuber, Alessandro Giusti, Luca Maria Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frédéric Precioso, Violet Snell, Josef Kittler, Teófilo Emídio de Campos, Adnan Mujahid Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim:
Assessment of algorithms for mitosis detection in breast cancer histopathology images. CoRR abs/1411.5825 (2014) - 2013
- [j57]Matthew D. Luciw, Varun Raj Kompella, Sohrob Kazerounian, Jürgen Schmidhuber:
An intrinsic value system for developing multiple invariant representations with incremental slowness learning. Frontiers Neurorobotics 7: 9 (2013) - [j56]Mikhail Frank, Jürgen Leitner, Marijn F. Stollenga, Alexander Förster, Jürgen Schmidhuber:
Curiosity driven reinforcement learning for motion planning on humanoids. Frontiers Neurorobotics 7: 25 (2013) - [j55]Ulrich Rührmair, Jan Sölter, Frank Sehnke, Xiaolin Xu, Ahmed Mahmoud, Vera Stoyanova, Gideon Dror, Jürgen Schmidhuber, Wayne P. Burleson, Srinivas Devadas:
PUF Modeling Attacks on Simulated and Silicon Data. IEEE Trans. Inf. Forensics Secur. 8(11): 1876-1891 (2013) - [c165]Bas R. Steunebrink, Jan Koutník, Kristinn R. Thórisson, Eric Nivel, Jürgen Schmidhuber:
Resource-Bounded Machines are Motivated to be Effective, Efficient, and Curious. AGI 2013: 119-129 - [c164]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Humanoid learns to detect its own hands. IEEE Congress on Evolutionary Computation 2013: 1411-1418 - [c163]Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving large-scale neural networks for vision-based TORCS. FDG 2013: 206-212 - [c162]Yi Sun, Tom Schaul, Faustino J. Gomez, Jürgen Schmidhuber:
A linear time natural evolution strategy for non-separable functions. GECCO (Companion) 2013: 61-62 - [c161]Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving large-scale neural networks for vision-based reinforcement learning. GECCO 2013: 1061-1068 - [c160]Jonathan Masci, Alessandro Giusti, Dan C. Ciresan, Gabriel Fricout, Jürgen Schmidhuber:
A fast learning algorithm for image segmentation with max-pooling convolutional networks. ICIP 2013: 2713-2717 - [c159]Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
Fast image scanning with deep max-pooling convolutional neural networks. ICIP 2013: 4034-4038 - [c158]Hung Quoc Ngo, Matthew David Luciw, Ngo Anh Vien, Jürgen Schmidhuber:
Upper Confidence Weighted Learning for Efficient Exploration in Multiclass Prediction with Binary Feedback. IJCAI 2013: 2488-2494 - [c157]Jürgen Leitner, Simon Harding, Mikhail Frank, Alexander Förster, Jürgen Schmidhuber:
Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots. IJCNN 2013: 1-7 - [c156]Jonathan Masci, Ueli Meier, Gabriel Fricout, Jürgen Schmidhuber:
Multi-scale pyramidal pooling network for generic steel defect classification. IJCNN 2013: 1-8 - [c155]Marijn F. Stollenga, Leo Pape, Mikhail Frank, Jürgen Leitner, Alexander Förster, Jürgen Schmidhuber:
Task-relevant roadmaps: A framework for humanoid motion planning. IROS 2013: 5772-5778 - [c154]Jonathan Masci, Jesús Angulo, Jürgen Schmidhuber:
A Learning Framework for Morphological Operators Using Counter-Harmonic Mean. ISMM 2013: 329-340 - [c153]Dan C. Ciresan, Alessandro Giusti, Luca Maria Gambardella, Jürgen Schmidhuber:
Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks. MICCAI (2) 2013: 411-418 - [c152]Rupesh Kumar Srivastava, Jonathan Masci, Sohrob Kazerounian, Faustino J. Gomez, Jürgen Schmidhuber:
Compete to Compute. NIPS 2013: 2310-2318 - [p6]Jürgen Schmidhuber:
Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity. Intrinsically Motivated Learning in Natural and Artificial Systems 2013: 95-128 - [i50]Jonathan Masci, Alessandro Giusti, Dan C. Ciresan, Gabriel Fricout, Jürgen Schmidhuber:
A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks. CoRR abs/1302.1690 (2013) - [i49]Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca Maria Gambardella, Jürgen Schmidhuber:
Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks. CoRR abs/1302.1700 (2013) - [i48]Somayeh Danafar, Paola M. V. Rancoita, Tobias Glasmachers, Kevin Whittingstall, Jürgen Schmidhuber:
Testing Hypotheses by Regularized Maximum Mean Discrepancy. CoRR abs/1305.0423 (2013) - [i47]Dan C. Ciresan, Jürgen Schmidhuber:
Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification. CoRR abs/1309.0261 (2013) - [i46]Jürgen Schmidhuber:
My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013. CoRR abs/1312.5548 (2013) - [i45]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Haris Dindo, Giovanni Pezzulo, Manuel Rodríguez, C. Hernández, Dimitri Ognibene, Jürgen Schmidhuber, Ricardo Sanz, Helgi Páll Helgason, Antonio Chella, Gudberg K. Jonsson:
Bounded Recursive Self-Improvement. CoRR abs/1312.6764 (2013) - [i44]Ulrich Rührmair, Jan Sölter, Frank Sehnke,