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ORCIDJürgen Schmidhuber
Person information
- affiliation: King Abdullah University of Science and Technology (KAUST), Saudi Arabia
- affiliation (former): University of Applied Sciences and Arts of Southern Switzerland, Switzerland
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2020 – today
- 2025
[e12]Djork-Arné Clevert
, Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
AI in Drug Discovery - First International Workshop, AIDD 2024, Held in Conjunction with ICANN 2024, Lugano, Switzerland, September 19, 2024, Proceedings. Lecture Notes in Computer Science 14894, Springer 2025, ISBN 978-3-031-72380-3 [contents]- 2024
- [j78]Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber:
Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter. IEEE Trans. Games 16(2): 384-395 (2024) - [j77]Lukas Tuggener, Raphael Emberger, Adhiraj Ghosh, Pascal Sager, Yvan Putra Satyawan, Javier A. Montoya-Zegarra, Simon Goldschagg, Florian Seibold, Urs Gut, Philipp Ackermann, Jürgen Schmidhuber, Thilo Stadelmann:
Real World Music Object Recognition. Trans. Int. Soc. Music. Inf. Retr. 7(1): 1-14 (2024) - [c254]Jinheng Xie, Songhe Deng, Bing Li, Haozhe Liu, Yawen Huang, Yefeng Zheng, Jürgen Schmidhuber, Bernard Ghanem, Linlin Shen, Mike Zheng Shou:
Tune-an-Ellipse: CLIP Has Potential to Find what you Want. CVPR 2024: 13723-13732 - [c253]Jürgen Schmidhuber:
Past, Present, Future, and Far Future of AI. DATA 2024: 9 - [c252]Kirolos Ataallah, Xiaoqian Shen, Eslam Abdelrahman, Essam Sleiman, Mingchen Zhuge, Jian Ding, Deyao Zhu, Jürgen Schmidhuber, Mohamed Elhoseiny:
Goldfish: Vision-Language Understanding of Arbitrarily Long Videos. ECCV (29) 2024: 251-267 - [c251]Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert:
Curating Reagents in Chemical Reaction Data with an Interactive Reagent Space Map. AIDD@ICANN 2024: 21-35 - [c250]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Self-organising Neural Discrete Representation Learning à la Kohonen. ICANN (1) 2024: 343-362 - [c249]Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber:
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework. ICLR 2024 - [c248]Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber:
Exploring the Promise and Limits of Real-Time Recurrent Learning. ICLR 2024 - [c247]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. ICML 2024 - [c246]Aditya A. Ramesh, Kenny John Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. ICML 2024 - [c245]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. ICML 2024 - [c244]Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang:
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays. ICML 2024 - [c243]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
GPTSwarm: Language Agents as Optimizable Graphs. ICML 2024 - [c242]Renzo Caballero, Piotr Piekos, Eric Feron, Jürgen Schmidhuber:
Utilizing a Malfunctioning 3D Printer by Modeling Its Dynamics with Machine Learning. ICRA 2024: 15562-15569 - [c241]Jürgen Schmidhuber:
Past, Present, Future, and Far Future of AI. ICSOFT 2024: 9 - [e11]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part I. Lecture Notes in Computer Science 15016, Springer 2024, ISBN 978-3-031-72331-5 [contents] - [e10]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part II. Lecture Notes in Computer Science 15017, Springer 2024, ISBN 978-3-031-72334-6 [contents] - [e9]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part III. Lecture Notes in Computer Science 15018, Springer 2024, ISBN 978-3-031-72337-7 [contents] - [e8]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IV. Lecture Notes in Computer Science 15019, Springer 2024, ISBN 978-3-031-72340-7 [contents] - [e7]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part V. Lecture Notes in Computer Science 15020, Springer 2024, ISBN 978-3-031-72343-8 [contents] - [e6]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VI. Lecture Notes in Computer Science 15021, Springer 2024, ISBN 978-3-031-72346-9 [contents] - [e5]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VII. Lecture Notes in Computer Science 15022, Springer 2024, ISBN 978-3-031-72349-0 [contents] - [e4]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part VIII. Lecture Notes in Computer Science 15023, Springer 2024, ISBN 978-3-031-72352-0 [contents] - [e3]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part IX. Lecture Notes in Computer Science 15024, Springer 2024, ISBN 978-3-031-72355-1 [contents] - [e2]Michael Wand, Kristína Malinovská, Jürgen Schmidhuber, Igor V. Tetko:
Artificial Neural Networks and Machine Learning - ICANN 2024 - 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17-20, 2024, Proceedings, Part X. Lecture Notes in Computer Science 15025, Springer 2024, ISBN 978-3-031-72358-2 [contents] - [i167]Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, Jürgen Schmidhuber:
Language Agents as Optimizable Graphs. CoRR abs/2402.16823 (2024) - [i166]Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber:
Learning Useful Representations of Recurrent Neural Network Weight Matrices. CoRR abs/2403.11998 (2024) - [i165]Wentian Zhang, Haozhe Liu, Jinheng Xie, Francesco Faccio, Mike Zheng Shou, Jürgen Schmidhuber:
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models. CoRR abs/2404.02747 (2024) - [i164]Mohannad Alhakami, Dylan R. Ashley, Joel Dunham, Francesco Faccio, Eric Feron, Jürgen Schmidhuber:
Towards a Robust Soft Baby Robot With Rich Interaction Ability for Advanced Machine Learning Algorithms. CoRR abs/2404.08093 (2024) - [i163]Haozhe Liu, Wentian Zhang, Bing Li, Bernard Ghanem, Jürgen Schmidhuber:
Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable. CoRR abs/2405.00466 (2024) - [i162]Aditya A. Ramesh, Kenny Young, Louis Kirsch, Jürgen Schmidhuber:
Sequence Compression Speeds Up Credit Assignment in Reinforcement Learning. CoRR abs/2405.03878 (2024) - [i161]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber, Christopher Potts, Christopher D. Manning:
MoEUT: Mixture-of-Experts Universal Transformers. CoRR abs/2405.16039 (2024) - [i160]Anand Gopalakrishnan, Aleksandar Stanic, Jürgen Schmidhuber, Michael Curtis Mozer:
Recurrent Complex-Weighted Autoencoders for Unsupervised Object Discovery. CoRR abs/2405.17283 (2024) - [i159]Yuhui Wang, Miroslav Strupl, Francesco Faccio, Qingyuan Wu, Haozhe Liu, Michal Grudzien, Xiaoyang Tan, Jürgen Schmidhuber:
Highway Reinforcement Learning. CoRR abs/2405.18289 (2024) - [i158]Yuhui Wang, Weida Li, Francesco Faccio, Qingyuan Wu, Jürgen Schmidhuber:
Highway Value Iteration Networks. CoRR abs/2406.03485 (2024) - [i157]Yuhui Wang, Qingyuan Wu, Weida Li, Dylan R. Ashley, Francesco Faccio, Chao Huang, Jürgen Schmidhuber:
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning. CoRR abs/2406.08404 (2024) - [i156]Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert:
Accelerating the inference of string generation-based chemical reaction models for industrial applications. CoRR abs/2407.09685 (2024) - [i155]Kirolos Ataallah, Xiaoqian Shen, Eslam Abdelrahman, Essam Sleiman, Mingchen Zhuge, Jian Ding, Deyao Zhu, Jürgen Schmidhuber, Mohamed Elhoseiny:
Goldfish: Vision-Language Understanding of Arbitrarily Long Videos. CoRR abs/2407.12679 (2024) - [i154]Xiuying Chen, Tairan Wang, Taicheng Guo, Kehan Guo, Juexiao Zhou, Haoyang Li, Mingchen Zhuge, Jürgen Schmidhuber, Xin Gao, Xiangliang Zhang:
ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering. CoRR abs/2407.16931 (2024) - [i153]Mingchen Zhuge, Changsheng Zhao, Dylan R. Ashley, Wenyi Wang, Dmitrii Khizbullin, Yunyang Xiong, Zechun Liu, Ernie Chang, Raghuraman Krishnamoorthi, Yuandong Tian, Yangyang Shi, Vikas Chandra, Jürgen Schmidhuber:
Agent-as-a-Judge: Evaluate Agents with Agents. CoRR abs/2410.10934 (2024) - [i152]Haozhe Liu, Shikun Liu, Zijian Zhou, Mengmeng Xu, Yanping Xie, Xiao Han, Juan C. Pérez, Ding Liu, Kumara Kahatapitiya, Menglin Jia, Jui-Chieh Wu, Sen He, Tao Xiang, Jürgen Schmidhuber, Juan-Manuel Pérez-Rúa:
MarDini: Masked Autoregressive Diffusion for Video Generation at Scale. CoRR abs/2410.20280 (2024) - [i151]Nanbo Li, Firas Laakom, Yucheng Xu, Wenyi Wang, Jürgen Schmidhuber:
FACTS: A Factored State-Space Framework For World Modelling. CoRR abs/2410.20922 (2024) - 2023
- [j76]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers. Neural Comput. 35(4): 593-626 (2023) - [c240]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. AAAI 2023: 7503-7511 - [c239]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
Approximating Two-Layer Feedforward Networks for Efficient Transformers. EMNLP (Findings) 2023: 674-692 - [c238]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. EMNLP 2023: 9455-9465 - [c237]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. ICCV 2023: 1955-1965 - [c236]Kazuki Irie, Jürgen Schmidhuber:
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. ICLR 2023 - [c235]Kenny John Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. ICML 2023: 40254-40276 - [c234]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-invented Experiments Encoded as Neural Networks. IWAI 2023: 254-274 - [c233]Aleksandar Stanic, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber:
Contrastive Training of Complex-Valued Autoencoders for Object Discovery. NeurIPS 2023 - [i150]Deyao Zhu, Yuhui Wang, Jürgen Schmidhuber, Mohamed Elhoseiny:
Guiding Online Reinforcement Learning with Action-Free Offline Pretraining. CoRR abs/2301.12876 (2023) - [i149]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Topological Neural Discrete Representation Learning à la Kohonen. CoRR abs/2302.07950 (2023) - [i148]Kazuki Irie, Jürgen Schmidhuber:
Accelerating Neural Self-Improvement via Bootstrapping. CoRR abs/2305.01547 (2023) - [i147]Imanol Schlag, Sainbayar Sukhbaatar, Asli Celikyilmaz, Wen-tau Yih, Jason Weston, Jürgen Schmidhuber, Xian Li:
Large Language Model Programs. CoRR abs/2305.05364 (2023) - [i146]Aleksandar Stanic, Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber:
Contrastive Training of Complex-Valued Autoencoders for Object Discovery. CoRR abs/2305.15001 (2023) - [i145]Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya A. Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber:
Mindstorms in Natural Language-Based Societies of Mind. CoRR abs/2305.17066 (2023) - [i144]Kazuki Irie, Anand Gopalakrishnan, Jürgen Schmidhuber:
Exploring the Promise and Limits of Real-Time Recurrent Learning. CoRR abs/2305.19044 (2023) - [i143]Haozhe Liu, Mingchen Zhuge, Bing Li, Yuhui Wang, Francesco Faccio, Bernard Ghanem, Jürgen Schmidhuber:
Learning to Identify Critical States for Reinforcement Learning from Videos. CoRR abs/2308.07795 (2023) - [i142]Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag:
The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute. CoRR abs/2309.11197 (2023) - [i141]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
Approximating Two-Layer Feedforward Networks for Efficient Transformers. CoRR abs/2310.10837 (2023) - [i140]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions. CoRR abs/2310.16076 (2023) - [i139]Joonsu Gha, Vincent Herrmann, Benjamin Grewe, Jürgen Schmidhuber, Anand Gopalakrishnan:
Unsupervised Musical Object Discovery from Audio. CoRR abs/2311.07534 (2023) - [i138]Lukas Tuggener, Thilo Stadelmann, Jürgen Schmidhuber:
Efficient Rotation Invariance in Deep Neural Networks through Artificial Mental Rotation. CoRR abs/2311.08525 (2023) - [i137]Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber:
Automating Continual Learning. CoRR abs/2312.00276 (2023) - [i136]Róbert Csordás, Piotr Piekos, Kazuki Irie, Jürgen Schmidhuber:
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention. CoRR abs/2312.07987 (2023) - 2022
- [j75]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it enough to optimize CNN architectures on ImageNet? Frontiers Comput. Sci. 4 (2022) - [j74]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) - [j73]Aditya A. Ramesh, Paulo E. Rauber, Michelangelo Conserva, Jürgen Schmidhuber:
Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits. Neural Comput. 34(11): 2232-2272 (2022) - [j72]Michael Wand, Morten Bak 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) - [c232]Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber:
Reward-Weighted Regression Converges to a Global Optimum. AAAI 2022: 8361-8369 - [c231]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations. EMNLP 2022: 9758-9767 - [c230]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. ICLR 2022 - [c229]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 - [c228]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. ICML 2022: 9660-9677 - [c227]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. NeurIPS 2022 - [c226]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. NeurIPS 2022 - [i135]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
A Modern Self-Referential Weight Matrix That Learns to Modify Itself. CoRR abs/2202.05780 (2022) - [i134]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) - [i133]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) - [i132]Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava:
Learning Relative Return Policies With Upside-Down Reinforcement Learning. CoRR abs/2202.12742 (2022) - [i131]Anand Gopalakrishnan, Kazuki Irie, Jürgen Schmidhuber, Sjoerd van Steenkiste:
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers. CoRR abs/2203.13573 (2022) - [i130]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) - [i129]Kazuki Irie, Francesco Faccio, Jürgen Schmidhuber:
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules. CoRR abs/2206.01649 (2022) - [i128]Francesco Faccio, Aditya A. 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) - [i127]Francesco Faccio, Vincent Herrmann, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
Goal-Conditioned Generators of Deep Policies. CoRR abs/2207.01570 (2022) - [i126]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) - [i125]Kazuki Irie, Jürgen Schmidhuber:
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules. CoRR abs/2210.06184 (2022) - [i124]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations. CoRR abs/2210.06350 (2022) - [i123]Kenny Young, Aditya A. Ramesh, Louis Kirsch, Jürgen Schmidhuber:
The Benefits of Model-Based Generalization in Reinforcement Learning. CoRR abs/2211.02222 (2022) - [i122]Kazuki Irie, Jürgen Schmidhuber:
Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks. CoRR abs/2211.09440 (2022) - [i121]Aditya A. Ramesh, Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Exploring through Random Curiosity with General Value Functions. CoRR abs/2211.10282 (2022) - [i120]Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber:
On Narrative Information and the Distillation of Stories. CoRR abs/2211.12423 (2022) - [i119]Jürgen Schmidhuber:
Annotated History of Modern AI and Deep Learning. CoRR abs/2212.11279 (2022) - [i118]Vincent Herrmann, Louis Kirsch, Jürgen Schmidhuber:
Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks. CoRR abs/2212.14374 (2022) - [i117]Louis Kirsch, Jürgen Schmidhuber:
Eliminating Meta Optimization Through Self-Referential Meta Learning. CoRR abs/2212.14392 (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) - [c225]Aleksandar Stanic, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Hierarchical Relational Inference. AAAI 2021: 9730-9738 - [c224]Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber:
The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers. EMNLP (1) 2021: 619-634 - [c223]Róbert Csordás, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks. ICLR 2021 - [c222]Francesco Faccio, Louis Kirsch, Jürgen Schmidhuber:
Parameter-Based Value Functions. ICLR 2021 - [c221]Anand Gopalakrishnan, Sjoerd van Steenkiste, Jürgen Schmidhuber:
Unsupervised Object Keypoint Learning using Local Spatial Predictability. ICLR 2021 - [c220]Ðorðe Miladinovic, Aleksandar Stanic, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann:
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling. ICLR 2021 - [c219]Imanol Schlag, Tsendsuren Munkhdalai, Jürgen Schmidhuber:
Learning Associative Inference Using Fast Weight Memory. ICLR 2021 - [c218]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021: 9355-9366 - [c217]Krsto Prorokovic, Michael Wand, Jürgen Schmidhuber:
Improving Stateful Premise Selection with Transformers. CICM 2021: 84-89 - [c216]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. NeurIPS 2021: 7703-7717 - [c215]Louis Kirsch, Jürgen Schmidhuber:
Meta Learning Backpropagation And Improving It. NeurIPS 2021: 14122-14134 - [i116]Imanol Schlag, Kazuki Irie, Jürgen Schmidhuber:
Linear Transformers Are Secretly Fast Weight Memory Systems. CoRR abs/2102.11174 (2021) - [i115]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) - [i114]Lukas Tuggener, Jürgen Schmidhuber, Thilo Stadelmann:
Is it Enough to Optimize CNN Architectures on ImageNet? CoRR abs/2103.09108 (2021) - [i113]Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber:
Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. CoRR abs/2106.06295 (2021) - [i112]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) - [i111]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) - [i110]