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Jürgen Schmidhuber
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- affiliation: University of Applied Sciences and Arts of Southern Switzerland, Switzerland
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2020 – today
- 2022
- [j73]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston
:
Bayesian Brains and the Rényi Divergence. Neural Comput. 34(4): 829-855 (2022) - [j72]Michael Wand
, Morten B. Kristoffersen
, Andreas W. Franzke
, Jürgen Schmidhuber:
Analysis of Neural Network Based Proportional Myoelectric Hand Prosthesis Control. IEEE Trans. Biomed. Eng. 69(7): 2283-2293 (2022) - [c226]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. AAAI 2022: 8361-8369 - [c225]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. ICML 2022: 9639-9659 - [c224]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. ICML 2022: 9660-9677 - [i116]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. CoRR abs/2202.05780 (2022) - [i115]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention. CoRR abs/2202.05798 (2022) - [i114]Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL. CoRR abs/2202.11960 (2022) - [i113]Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR abs/2202.12742 (2022) - [i112]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers. CoRR abs/2203.13573 (2022) - [i111]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets. CoRR abs/2205.06595 (2022) - [i110]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. CoRR abs/2206.01649 (2022) - [i109]Francesco Faccio, Aditya Ramesh, Vincent Herrmann, Jean Harb, Jürgen Schmidhuber:
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States. CoRR abs/2207.01566 (2022) - [i108]Francesco Faccio, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. CoRR abs/2207.01570 (2022) - [i107]Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber:
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter. CoRR abs/2208.03374 (2022) - 2021
- [j71]Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients. Neural Comput. 33(6): 1498-1553 (2021) - [j70]Ariel Ruiz-Garcia, Jürgen Schmidhuber, Vasile Palade, Clive Cheong Took, Danilo P. Mandic:
Deep neural network representation and Generative Adversarial Learning. Neural Networks 139: 199-200 (2021) - [c223]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. AAAI 2021: 9730-9738 - [c222]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. EMNLP (1) 2021: 619-634 - [c221]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. ICLR 2021 - [c220]Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber:
Parameter-Based Value Functions. ICLR 2021 - [c219]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. ICLR 2021 - [c218]Ðorðe Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. ICLR 2021 - [c217]Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber:
Learning Associative Inference Using Fast Weight Memory. ICLR 2021 - [c216]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021: 9355-9366 - [c215]Krsto Prorokovic, Michael Wand, Jürgen Schmidhuber:
Improving Stateful Premise Selection with Transformers. CICM 2021: 84-89 - [c214]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. NeurIPS 2021: 7703-7717 - [c213]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. NeurIPS 2021: 14122-14134 - [i106]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Memory Systems. CoRR abs/2102.11174 (2021) - [i105]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) - [i104]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it Enough to Optimize CNN Architectures on ImageNet? CoRR abs/2103.09108 (2021) - [i103]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. CoRR abs/2106.06295 (2021) - [i102]Noor Sajid, Francesco Faccio, Lancelot Da Costa, Thomas Parr, Jürgen Schmidhuber, Karl J. Friston:
Bayesian brains and the Rényi divergence. CoRR abs/2107.05438 (2021) - [i101]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley
, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. CoRR abs/2107.09088 (2021) - [i100]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. CoRR abs/2108.12284 (2021) - [i99]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. CoRR abs/2110.07732 (2021) - [i98]Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber:
Automatic Embedding of Stories Into Collections of Independent Media. CoRR abs/2111.02216 (2021) - [i97]Kazuki Irie, Jürgen Schmidhuber:
Training and Generating Neural Networks in Compressed Weight Space. CoRR abs/2112.15545 (2021) - [i96]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Improving Baselines in the Wild. CoRR abs/2112.15550 (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) - [c212]Matteo Riva, Michael Wand, Jürgen Schmidhuber:
Motion Dynamics Improve Speaker-Independent Lipreading. ICASSP 2020: 4407-4411 - [c211]Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Improving Generalization in Meta Reinforcement Learning using Learned Objectives. ICLR 2020 - [c210]Lukas Tuggener, Yvan Putra Satyawan
, Alexander Pacha, Jürgen Schmidhuber, Thilo Stadelmann:
The DeepScoresV2 Dataset and Benchmark for Music Object Detection. ICPR 2020: 9188-9195 - [c209]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
- [c208]Krsto Prorokovic, Michael Wand, Tanja Schultz, Jürgen Schmidhuber:
Adaptation of an EMG-Based Speech Recognizer via Meta-Learning. GlobalSIP 2019: 1-5 - [c207]Róbert Csordás, Jürgen Schmidhuber:
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control. ICLR (Poster) 2019 - [c206]Paulo E. Rauber, Avinash Ummadisingu, Filipe Mutz, Jürgen Schmidhuber:
Hindsight policy gradients. ICLR (Poster) 2019 - [c205]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
- [c204]Michael Wand, Jürgen Schmidhuber, Ngoc Thang Vu:
Investigations on End- to-End Audiovisual Fusion. ICASSP 2018: 3041-3045 - [c203]Sjoerd van Steenkiste, Michael Chang, Klaus Greff, Jürgen Schmidhuber:
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions. ICLR (Poster) 2018 - [c202]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 - [c201]Michael Wand, Tanja Schultz
, Jürgen Schmidhuber:
Domain-Adversarial Training for Session Independent EMG-based Speech Recognition. INTERSPEECH 2018: 3167-3171 - [c200]Lukas Tuggener, Ismail Elezi, Jürgen Schmidhuber, Thilo Stadelmann:
Deep Watershed Detector for Music Object Recognition. ISMIR 2018: 271-278 - [c199]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. NeurIPS 2018: 2455-2467 - [c198]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) - [c197]Klaus Greff, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Highway and Residual Networks learn Unrolled Iterative Estimation. ICLR (Poster) 2017 - [c196]Klaus Greff, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Neural Expectation Maximization. ICLR (Workshop) 2017 - [c195]Julian Georg Zilly, Rupesh Kumar Srivastava, Jan Koutník, Jürgen Schmidhuber:
Recurrent Highway Networks. ICML 2017: 4189-4198 - [c194]Michael Wand, Jürgen Schmidhuber:
Improving Speaker-Independent Lipreading with Domain-Adversarial Training. INTERSPEECH 2017: 3662-3666 - [c193]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) - [c192]Bas R. Steunebrink, Kristinn R. Thórisson, Jürgen Schmidhuber:
Growing Recursive Self-Improvers. AGI 2016: 129-139 - [c191]Sjoerd van Steenkiste
, Jan Koutník, Kurt Driessens, Jürgen Schmidhuber:
A Wavelet-based Encoding for Neuroevolution. GECCO 2016: 517-524 - [c190]Michael Wand, Jan Koutník, Jürgen Schmidhuber:
Lipreading with long short-term memory. ICASSP 2016: 6115-6119 - [c189]Michael Wand, Jürgen Schmidhuber:
Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition. INTERSPEECH 2016: 3032-3036 - [c188]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) - [c187]Eric Nivel, Kristinn R. Thórisson, Bas R. Steunebrink, Jürgen Schmidhuber:
Anytime Bounded Rationality. AGI 2015: 121-130 - [c186]Marijn F. Stollenga, Alan J. Lockett, Jürgen Schmidhuber:
The Natural Gradient as a control signal for a humanoid robot. Humanoids 2015: 187-193 - [c185]Rupesh Kumar Srivastava, Klaus Greff, Jürgen Schmidhuber:
Training Very Deep Networks. NIPS 2015: 2377-2385 - [c184]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 - [c183]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) - [c182]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 - [c181]Jan Koutník, Jürgen Schmidhuber, Faustino J. Gomez:
Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. GECCO 2014: 541-548 - [c180]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 - [c179]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 - [c178]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 - [c177]Jan Koutník, Klaus Greff, Faustino J. Gomez, Jürgen Schmidhuber:
A Clockwork RNN. ICML 2014: 1863-1871 - [c176]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 - [c175]