J. Schmidhuber

Jürgen Schmidhuber, Bas R. Steunebrink, Rupesh Kumar Srivastava, 2012, Neural networks : the official journal of the International Neural Network Society.

Learning to Forget: Continual Prediction with LSTM

Jürgen Schmidhuber, Fred A. Cummins, Felix A. Gers, 2000, Neural Computation.

Learning Complex, Extended Sequences Using the Principle of History Compression

Jürgen Schmidhuber, J. Schmidhuber, 1992, Neural Computation.

Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability

Jürgen Schmidhuber, J. Schmidhuber, 1997, Neural Networks.

Reducing the Ratio Between Learning Complexity and Number of Time Varying Variables in Fully Recurrent Nets

J. Schmidhuber, 1993 .

Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity & Creativity pdf

Jürgen Schmidhuber, J. Schmidhuber, 2007, Discovery Science.

Fitness Expectation Maximization

Tom Schaul, Jürgen Schmidhuber, Jan Peters, 2008, PPSN.

The Sacred Infrastructure for Computational Research

J. Schmidhuber, F. Hutter, Klaus Greff, 2017, SciPy.

State-Dependent Exploration for Policy Gradient Methods

Jürgen Schmidhuber, Martin Felder, Thomas Rückstieß, 2008, ECML/PKDD.

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

Jürgen Schmidhuber, J. Schmidhuber, 2008, ABiALS.

Evolving large-scale neural networks for vision-based reinforcement learning

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2013, GECCO '13.

Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Flexible, High Performance Convolutional Neural Networks for Image Classification

Luca Maria Gambardella, Jürgen Schmidhuber, Jonathan Masci, 2022 .

Solving Deep Memory POMDPs with Recurrent Policy Gradients

Jürgen Schmidhuber, Alexander Förster, Jan Peters, 2007, ICANN.

Frontier Search

J. Schmidhuber, T. Schaul, Sun Yi, 2009 .

Flat Minima

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1997, Neural Computation.

Recurrent policy gradients

Jürgen Schmidhuber, Alexander Förster, Jan Peters, 2010, Log. J. IGPL.

Accelerated Neural Evolution through Cooperatively Coevolved Synapses

Risto Miikkulainen, Jürgen Schmidhuber, Faustino J. Gomez, 2008, J. Mach. Learn. Res..

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Jürgen Schmidhuber, Flavio Fontana, Davide Scaramuzza, 2016, IEEE Robotics and Automation Letters.

Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2011, AGI.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006 .

Recurrent Highway Networks pdf

Jürgen Schmidhuber, Jan Koutník, Rupesh Kumar Srivastava, 2016, ICML.

Exploring parameter space in reinforcement learning

Tom Schaul, Yi Sun, Frank Sehnke, 2010, Paladyn J. Behav. Robotics.

Compete to Compute

Jürgen Schmidhuber, Jonathan Masci, Faustino J. Gomez, 2013, NIPS.

Optimal Artiﬁcial Curiosity, Creativity, Music, and the Fine Arts

Jürgen Schmidhuber, J. Schmidhuber, J¨urgen Schmidhuber, 2005 .

Low Complexity Proto-Value Function Learning from Sensory Observations with Incremental Slow Feature Analysis

Jürgen Schmidhuber, Matthew D. Luciw, J. Schmidhuber, 2012, ICANN.

Intrinsically motivated neuroevolution for vision-based reinforcement learning

Jurgen Schmidhuber, Giuseppe Cuccu, Matthew Luciw, 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

Learning Precise Timing with LSTM Recurrent Networks

Jürgen Schmidhuber, Nicol N. Schraudolph, Felix A. Gers, 2003, J. Mach. Learn. Res..

The IM-CLeVeR Project : Intrinsically Motivated Cumulative Learning Versatile Robots

Julian Togelius, Francesco Mannella, Jürgen Schmidhuber, 2009 .

A robot that reinforcement-learns to identify and memorize important previous observations

Jürgen Schmidhuber, Gabriel Gruener, Bram Bakker, 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

Semilinear Predictability Minimization Produces Well-Known Feature Detectors

Jürgen Schmidhuber, Martin Eldracher, Bernhard Foltin, 1996, Neural Computation.

PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

Jürgen Schmidhuber, J. Schmidhuber, 2011, Front. Psychol..

A Clockwork RNN

Jürgen Schmidhuber, Jan Koutník, Klaus Greff, 2014, ICML.

Training Recurrent Networks by Evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2007, Neural Computation.

Crowdsourcing the creation of image segmentation algorithms for connectomics

Joachim M. Buhmann, Changming Sun, H. Sebastian Seung, 2015, Front. Neuroanat..

Learning Team Strategies: Soccer Case Studies

Jürgen Schmidhuber, Marco Wiering, Rafal Salustowicz, 1998, Machine Learning.

Exponential natural evolution strategies

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2010, GECCO '10.

Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions pdf

Jürgen Schmidhuber, Klaus Greff, Sjoerd van Steenkiste, 2018, ICLR.

Evolino: Hybrid Neuroevolution/Optimal Linear Search for Sequence Learning

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, IJCAI.

Modeling systems with internal state using evolino

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, GECCO '05.

LSTM can Solve Hard Long Time Lag Problems

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1996, NIPS.

A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks

Jürgen Schmidhuber, Alois Knoll, Faustino J. Gomez, 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Multi-column deep neural networks for image classification

Jürgen Schmidhuber, Ueli Meier, Dan C. Ciresan, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Deep learning in neural networks: An overview

Jürgen Schmidhuber, J. Schmidhuber, 2014, Neural Networks.

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Jürgen Schmidhuber, Alex Graves, J. Schmidhuber, 2005, Neural Networks.

Measuring Intelligence through Games pdf

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, 2011, ArXiv.

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Luca Maria Gambardella, Jürgen Schmidhuber, Alessandro Giusti, 2013, MICCAI.

World Models pdf

Jürgen Schmidhuber, David Ha, J. Schmidhuber, 2018, ArXiv.

A committee of neural networks for traffic sign classification

Jürgen Schmidhuber, Jonathan Masci, Ueli Meier, 2011, The 2011 International Joint Conference on Neural Networks.

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2006, ICML.

Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images

Luca Maria Gambardella, Jürgen Schmidhuber, Alessandro Giusti, 2012, NIPS.

Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks

Jürgen Schmidhuber, Marcus Liwicki, Horst Bunke, 2007, NIPS.

Policy Gradients with Parameter-Based Exploration for Control

Frank Sehnke, Christian Osendorfer, Jürgen Schmidhuber, 2008, ICANN.

Feature Extraction Through LOCOCODE

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1999, Neural Computation.

Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2005, ICANN.

Stochastic search using the natural gradient

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2009, ICML '09.

Convolutional Neural Network Committees for Handwritten Character Classification

Luca Maria Gambardella, Jürgen Schmidhuber, Ueli Meier, 2011, 2011 International Conference on Document Analysis and Recognition.

Evolino for recurrent support vector machines pdf

Jürgen Schmidhuber, Faustino J. Gomez, Daan Wierstra, 2005, ESANN.

Evolving Memory Cell Structures for Sequence Learning

Julian Togelius, Jürgen Schmidhuber, Justin Bayer, 2009, ICANN.

Evolino: Hybrid Neuroevolution / Optimal Linear Search for Sequence Prediction

Jürgen Schmidhuber, Daan Wierstra, Faustino Gomez, 2005, IJCAI 2005.

Robust player imitation using multiobjective evolution

Julian Togelius, Jürgen Schmidhuber, Daan Wierstra, 2009, 2009 IEEE Congress on Evolutionary Computation.

Continuous history compression

Jürgen Schmidhuber, Michael C. Mozer, D. Prelinger, 1993 .

A local learning algorithm for dynamic feedforward and recurrent networks

Jürgen Schmidhuber, J. Schmidhuber, 1990, Forschungsberichte, TU Munich.

Learning Factorial Codes by Predictability Minimization

Jürgen Schmidhuber, J. Schmidhuber, 1992, Neural Computation.

Learning to forget: continual prediction with LSTM

Jürgen Schmidhuber, Fred Cummins, Felix A. Gers, 1999 .

Multi-Dimensional Deep Memory Go-Player for Parameter Exploring Policy Gradients

J. Schmidhuber, T. Schaul, Frank Sehnke, 2010 .

Parameter-based Value Functions pdf

Francesco Faccio, J. Schmidhuber, Jurgen Schmidhuber, 2020, ICLR.

Better Digit Recognition with a Committee of Simple Neural Nets

Luca Maria Gambardella, Jürgen Schmidhuber, Ueli Meier, 2011, 2011 International Conference on Document Analysis and Recognition.

Deep Networks with Internal Selective Attention through Feedback Connections

Jürgen Schmidhuber, Marijn F. Stollenga, Jonathan Masci, 2014, NIPS.

On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models pdf

Jürgen Schmidhuber, J. Schmidhuber, 2015, ArXiv.

Sequential neural text compression

Jürgen Schmidhuber, Stefan Heil, J. Schmidhuber, 1996, IEEE Trans. Neural Networks.

Parameter-exploring policy gradients

Frank Sehnke, Christian Osendorfer, Jürgen Schmidhuber, 2010, Neural Networks.

One Big Net For Everything pdf

Jürgen Schmidhuber, J. Schmidhuber, 2018, ArXiv.

Learning to Generate Artificial Fovea Trajectories for Target Detection

Jürgen Schmidhuber, Rudolf Huber, J. Schmidhuber, 1991, Int. J. Neural Syst..

LSTM recurrent networks learn simple context-free and context-sensitive languages

Jürgen Schmidhuber, Felix A. Gers, J. Schmidhuber, 2001, IEEE Trans. Neural Networks.

Efficient Non-linear Control Through Neuroevolution

Risto Miikkulainen, Jürgen Schmidhuber, Faustino J. Gomez, 2006, ECML.

LSTM: A Search Space Odyssey

Jürgen Schmidhuber, Klaus Greff, Bas R. Steunebrink, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Highway and Residual Networks learn Unrolled Iterative Estimation pdf

Jürgen Schmidhuber, Klaus Greff, Rupesh Kumar Srivastava, 2016, ICLR.

Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

Jürgen Schmidhuber, Alex Graves, J. Schmidhuber, 2008, NIPS.

Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集高次機能の学習と創発--脳・ロボット・人間研究における新たな展開)

Jürgen Schmidhuber, J. Schmidhuber, 2009 .

RNN-based Learning of Compact Maps for Efficient Robot Localization

Jürgen Schmidhuber, Alex Graves, Alexander Förster, 2007, ESANN.

Learning to Reason with Third-Order Tensor Products

Jürgen Schmidhuber, Imanol Schlag, J. Schmidhuber, 2018, NeurIPS.

High dimensions and heavy tails for natural evolution strategies

Tom Schaul, Jürgen Schmidhuber, Tobias Glasmachers, 2011, GECCO '11.

Understanding Locally Competitive Networks pdf

Jürgen Schmidhuber, Jonathan Masci, Faustino J. Gomez, 2014, ICLR.

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

Jürgen Schmidhuber, Marcus Liwicki, Marijn F. Stollenga, 2015, NIPS.

Searching for Minimal Neural Networks in Fourier Space

Jürgen Schmidhuber, Jan Koutník, Faustino Gomez, 2010, AGI 2010.

Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets

Jürgen Schmidhuber, Douglas Eck, Felix A. Gers, 2003, Neural Networks.

Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2007, IJCAI.

Algorithmic Theories of Everything

Jürgen Schmidhuber, J. Schmidhuber, 2000, ArXiv.

A comparison between spiking and differentiable recurrent neural networks on spoken digit recognition

Jürgen Schmidhuber, Alex Graves, Nicole Beringer, 2004, Neural Networks and Computational Intelligence.

A comparison of algorithms and humans for mitosis detection

Luca Maria Gambardella, Jürgen Schmidhuber, Alessandro Giusti, 2014, 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI).

Neural Networks for Segmenting Neuronal Structures in EM Stacks

J. Schmidhuber, A. Giusti, D. Ciresan, 2012 .

Transfer learning for Latin and Chinese characters with Deep Neural Networks

Jürgen Schmidhuber, Ueli Meier, Dan C. Ciresan, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Optimal Ordered Problem Solver

Jürgen Schmidhuber, J. Schmidhuber, 2002, Machine Learning.

Ultimate Cognition à la Gödel

Jürgen Schmidhuber, J. Schmidhuber, 2009, Cognitive Computation.

Hierarchies of Generalized Kolmogorov Complexities and Nonenumerable Universal Measures Computable in the Limit

Jürgen Schmidhuber, J. Schmidhuber, 2002, Int. J. Found. Comput. Sci..

Low-Complexity Art

Jürgen Schmidhuber, J. Schmidhuber, 2017 .

Quasi-online reinforcement learning for robots

Jürgen Schmidhuber, Gabriel Gruener, Bram Bakker, 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

Multidimensional Recurrent Neural Networks

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2007 .

Formal Theory of Fun and Creativity

Jürgen Schmidhuber, J. Schmidhuber, 2010, ECML/PKDD.

Completely Self-referential Optimal Reinforcement Learners

Jürgen Schmidhuber, J. Schmidhuber, 2005, ICANN.

Applying LSTM to Time Series Predictable through Time-Window Approaches

Jürgen Schmidhuber, Felix A. Gers, Douglas Eck, 2000, ICANN.

Lipreading with long short-term memory

Jürgen Schmidhuber, Jan Koutník, Michael Wand, 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Deep Big Multilayer Perceptrons for Digit Recognition

Luca Maria Gambardella, Jürgen Schmidhuber, Ueli Meier, 2012, Neural Networks: Tricks of the Trade.

Super mario evolution

Julian Togelius, Jürgen Schmidhuber, Jan Koutník, 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

Finding temporal structure in music: blues improvisation with LSTM recurrent networks

Jürgen Schmidhuber, Douglas Eck, J. Schmidhuber, 2002, Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing.

Tagger: Deep Unsupervised Perceptual Grouping

Harri Valpola, Jürgen Schmidhuber, Klaus Greff, 2016, NIPS.

Reinforcement learning in partially observable mobile robot domains using unsupervised event extraction

Jürgen Schmidhuber, Bram Bakker, Fredrik Linåker, 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

Unsupervised Modeling of Partially Observable Environments

Jürgen Schmidhuber, Jan Koutník, Vincent Graziano, 2011, ECML/PKDD.

Curiosity driven reinforcement learning for motion planning on humanoids

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2014, Front. Neurorobot..

Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning

Julian Togelius, Jürgen Schmidhuber, Faustino J. Gomez, 2009, ICANN.

An Application of Recurrent Neural Networks to Discriminative Keyword Spotting

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2007, ICANN.

Neural Expectation Maximization

Jürgen Schmidhuber, Klaus Greff, Sjoerd van Steenkiste, 2017, NIPS.

Solving POMDPs with Levin Search and EIRA

Jürgen Schmidhuber, Marco Wiering, J. Schmidhuber, 1996, ICML.

Learning skills from play: Artificial curiosity on a Katana robot arm

Jürgen Schmidhuber, Alexander Förster, Matthew D. Luciw, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Fast Online Q(λ)

Jürgen Schmidhuber, Marco Wiering, J. Schmidhuber, 1998, Machine Learning.

Recurrent Neural Processes pdf

Christian Osendorfer, Jonathan Masci, Jurgen Schmidhuber, 2019, ArXiv.

An experiment in automatic game design

Julian Togelius, Jürgen Schmidhuber, J. Schmidhuber, 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

Jürgen Schmidhuber, Alexander M. Bronstein, Jonathan Masci, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Unconventional computing using evolution-in-nanomaterio: neural networks meet nanoparticle networks

Jan Koutník, Klaus Greff, Haitze J. Broersma, 2016 .

Classifying Unprompted Speech by Retraining LSTM Nets

Jürgen Schmidhuber, Alex Graves, Nicole Beringer, 2005, ICANN.

A Computer Scientist's View of Life, the Universe, and Everything

Jürgen Schmidhuber, J. Schmidhuber, 1999, Foundations of Computer Science: Potential - Theory - Cognition.

Discovering Boolean Gates in Slime Mould pdf

Jürgen Schmidhuber, Andrew Adamatzky, Jan Koutník, 2016, ArXiv.

The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions

Jürgen Schmidhuber, J. Schmidhuber, Jürgen Schmidhuber, 2002, COLT.

Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements

Jürgen Schmidhuber, J. Schmidhuber, 2003, ArXiv.

Planning simple trajectories using neural subgoal generators

Jürgen Schmidhuber, Reiner Wahnsiedler, J. Schmidhuber, 1993 .

Gödel Machines: Fully Self-referential Optimal Universal Self-improvers

Jürgen Schmidhuber, J. Schmidhuber, 2007, Artificial General Intelligence.

Biologically Plausible Speech Recognition with LSTM Neural Nets

Jürgen Schmidhuber, Douglas Eck, Alex Graves, 2004, BioADIT.

Artificial curiosity based on discovering novel algorithmic predictability through coevolution

Jürgen Schmidhuber, J. Schmidhuber, 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space

Jürgen Schmidhuber, Rafal Salustowicz, J. Schmidhuber, 1997, ECML.

Max-pooling convolutional neural networks for vision-based hand gesture recognition

Luca Maria Gambardella, Farrukh Nagi, Jürgen Schmidhuber, 2011, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

A fast learning algorithm for image segmentation with max-pooling convolutional networks

Jürgen Schmidhuber, Jonathan Masci, Alessandro Giusti, 2013, 2013 IEEE International Conference on Image Processing.

Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Self-organizing nets for optimization

Jürgen Schmidhuber, Michele Milano, Petros Koumoutsakos, 2004, IEEE Transactions on Neural Networks.

Autonomous learning of robust visual object detection and identification on a humanoid

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Maximizing Fun by Creating Data with Easily Reducible Subjective Complexity

Jürgen Schmidhuber, J. Schmidhuber, 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

Mars terrain image classification using Cartesian genetic programming

Jürgen Leitner, Jürgen Schmidhuber, Alexander Förster, 2012 .

Hierarchical reinforcement learning with subpolicies specializing for learned subgoals

Jürgen Schmidhuber, Bram Bakker, J. Schmidhuber, 2004, Neural Networks and Computational Intelligence.

Accelerated learning in back-propagation nets

Jürgen Schmidhuber, J. Schmidhuber, 1989 .

Learning dynamic algorithm portfolios

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2006, Annals of Mathematics and Artificial Intelligence.

Gambling in a Computationally Expensive Casino : Algorithm Selection as a Bandit Problem

J. Schmidhuber, M. Gagliolo, 2006 .

Language identification from prosody without explicit features

Jürgen Schmidhuber, Fred A. Cummins, Felix A. Gers, 1999, EUROSPEECH.

Steel defect classification with Max-Pooling Convolutional Neural Networks

Jürgen Schmidhuber, Jonathan Masci, Gabriel Fricout, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

On the Size of the Online Kernel Sparsification Dictionary pdf

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2012, ICML.

Greatest Thing Since...: What is the Most Significant Invention of the Past Decade, and What Do You Anticipate for the Future?

J. Schmidhuber, A. D. Narasimhalu, Frederick K. Balagaddé, 2011 .

Training Agents using Upside-Down Reinforcement Learning

Filipe Wall Mutz, J. Schmidhuber, R. Srivastava, 2019, ArXiv.

Scaling up Multidimensional Recurrent Neural Networks for image segmentation

J. Schmidhuber, R. Srivastava, Klaus Greff, 2016 .

The New AI is General & Mathematically Rigorous

J. Schmidhuber, 2010 .

Recurrent Support Vector Machines

Faustino J. Gomez, J. Schmidhuber, Daan Wierstra, 2005 .

1 5 D ec 2 00 5 Evolino for Recurrent Support Vector Machines

Faustino J. Gomez, J. Schmidhuber, Daan Wierstra, 2022 .

The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2021, ArXiv.

A possibility for implementing curiosity and boredom in model-building neural controllers

Jürgen Schmidhuber, J. Schmidhuber, 1991 .

Accumulator Intrinsic Motivation Block φ L 1 φ L k Input Observations ( x ) Reward Switch

Marijn F. Stollenga, Varun Raj Kompella, J. Schmidhuber, 2012 .

UNROLLED ITERATIVE ESTIMATION

J. Schmidhuber, R. Srivastava, 2017 .

Compressed Network Complexity Search

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2012, PPSN.

Using Neural Networks to Predict the Functionality of Reconfigurable Nanomaterial Networks

Celestine Preetham Lawrence, J. Schmidhuber, Klaus Greff, 2017 .

Algorithm 1 Curiosity-driven Optimization

J. Schmidhuber, T. Schaul, Daan Wierstra, 2011 .

Towards Practical Universal Search Tom

J. Schmidhuber, 2010 .

EVALUATING BENCHMARK PROBLEMS BY RANDOM GUESSING

Jürgen Schmidhuber, J. Schmidhuber, 2015 .

Meta-Learning for Recalibration of EMG-Based Upper Limb Prostheses

J. Schmidhuber, Michael Wand, Krsto Prorokovic, 2020 .

Low-complexity Art Low-complexity Art Report Fki-197-94 (revised)

J. Schmidhuber, urgen Schmidhuber, 1995 .

Five Answers on Randomness

J. Schmidhuber, 2009 .

A Novel Connectionist System for Unconstrained Handwriting Recognition

J. Schmidhuber, A. Graves, M. Liwicki, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Autocatalytic endogenous reflective architecture

Jürgen Schmidhuber, Antonio Chella, Giovanni Pezzulo, 2013 .

No . IDSIA-04-08 1 Phoneme recognition in TIMIT with BLSTM-CTC

J. Schmidhuber, A. Graves, Santiago Fernández, 2008 .

Artificial General Intelligence. Proceedings of the 4th International Conference

Jürgen Schmidhuber, Kristinn R. Thórisson, Moshe Looks, 2011 .

Proceedings of the 4th international conference on Artificial general intelligence

Jürgen Schmidhuber, Kristinn R. Thórisson, Moshe Looks, 2011 .

The neural bucket brigade

Jürgen Schmidhuber, J. Schmidhuber, 1989 .

Learning to generate sub-goals for action sequences

Jürgen Schmidhuber, J. Schmidhuber, 1991 .

Learning Relative Return Policies With Upside-Down Reinforcement Learning

Dylan R. Ashley, J. Schmidhuber, R. Srivastava, 2022, ArXiv.

Deep Learning

Jürgen Schmidhuber, J. Schmidhuber, 2015, Encyclopedia of Machine Learning and Data Mining.

Turing: Keep his work in perspective

Jürgen Schmidhuber, J. Schmidhuber, 2012, Nature.

The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2021, ICLR.

Recurrent networks adjusted by adaptive critics

Jürgen Schmidhuber, J. Schmidhuber, 1990 .

Improving Stateful Premise Selection with Transformers

Jürgen Schmidhuber, Michael Wand, J. Schmidhuber, 2021, International Conference on Intelligent Computer Mathematics.

Networks adjusting networks

Jürgen Schmidhuber, J. Schmidhuber, 1990, Forschungsberichte, TU Munich.

Randomness, Occam's Razor, AI, Creativity and Digital Physics

Jürgen Schmidhuber, J. Schmidhuber, 2011 .

Evolving Structured Programs with Hierarchical Instructions and Skip Nodes

Jürgen Schmidhuber, Rafal Salustowicz, J. Schmidhuber, 1998, ICML.

Artificial General Intelligence - 4th International Conference, AGI 2011, Mountain View, CA, USA, August 3-6, 2011. Proceedings

Jürgen Schmidhuber, Kristinn R. Thórisson, Moshe Looks, 2011, AGI.

Deep neural network representation and Generative Adversarial Learning

Jürgen Schmidhuber, Danilo P. Mandic, Vasile Palade, 2021, Neural Networks.

Autonomous reinforcement of behavioral sequences in neural dynamics

J. Schmidhuber, M. Luciw, G. Schöner, 2012, The 2013 International Joint Conference on Neural Networks (IJCNN).

Augmenting Classic Algorithms with Neural Components for Strong Generalisation on Ambiguous and High-Dimensional Data

J. Schmidhuber, Imanol Schlag, 2021 .

Learning Adaptive Control Flow in Transformers for Improved Systematic Generalization

J. Schmidhuber, Kazuki Irie, R. Csordás, 2021 .

Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules

J. Schmidhuber, Francesco Faccio, Kazuki Irie, 2022, NeurIPS.

Using sequential adaptive neuro-control for efficient learning of rotation and translation invariance

Jürgen Schmidhuber, Jürgen Schmidhuber, Rudolf M. Huber, 1991 .

A Technical Justification of Consciousness

Jürgen Schmidhuber, J. Schmidhuber, 2005 .

2006: Celebrating 75 Years of AI - History and Outlook: The Next 25 Years

J. Schmidhuber, 2007, 50 Years of Artificial Intelligence.

The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2022, ICML.

General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States

J. Schmidhuber, A. Ramesh, J. Harb, 2022, ArXiv.

Goal-Conditioned Generators of Deep Policies

J. Schmidhuber, A. Ramesh, Francesco Faccio, 2022, AAAI.

A Modern Self-Referential Weight Matrix That Learns to Modify Itself

J. Schmidhuber, Imanol Schlag, Kazuki Irie, 2022, ICML.

Evolving Cell Structures for Sequence Learning

Julian Togelius, Jürgen Schmidhuber, Justin Bayer, 2009, ICANN 2009.

Improving Generalization in Meta Reinforcement Learning using Learned Objectives

Sjoerd van Steenkiste, J. Schmidhuber, Louis Kirsch, 2019, ICLR.

CTL++: Evaluating Generalization on Never-Seen Compositional Patterns of Known Functions, and Compatibility of Neural Representations

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2022, Conference on Empirical Methods in Natural Language Processing.

Are Neural Nets Modular? Inspecting Their Functionality Through Differentiable Weight Masks

Sjoerd van Steenkiste, J. Schmidhuber, R. Csordás, 2020 .

Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules

J. Schmidhuber, Kazuki Irie, 2022, ArXiv.

Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter

J. Schmidhuber, David Ha, Yujin Tang, 2022, IEEE Transactions on Games.

An Investigation into the Open World Survival Game Crafter

J. Schmidhuber, David Ha, Yujin Tang, 2022 .

Dekf-lstm

J. Schmidhuber, Douglas Eck, Juan Antonio Pérez-Ortiz, 2002, ESANN.

Learning to Control Rapidly Changing Synaptic Connections: An Alternative Type of Memory in Sequence Processing Artificial Neural Networks

J. Schmidhuber, Kazuki Irie, 2022, arXiv.org.

Exploring through Random Curiosity with General Value Functions

Sjoerd van Steenkiste, J. Schmidhuber, A. Ramesh, 2022, NeurIPS.

The Benefits of Model-Based Generalization in Reinforcement Learning

J. Schmidhuber, A. Ramesh, Louis Kirsch, 2022, ArXiv.

Unsupervised Learning of Temporal Abstractions With Slot-Based Transformers

Sjoerd van Steenkiste, J. Schmidhuber, Kazuki Irie, 2022, Neural Computation.

Learning One Abstract Bit at a Time Through Self-Invented Experiments Encoded as Neural Networks

J. Schmidhuber, Louis Kirsch, Vincent Herrmann, 2022, IWAI.

On Narrative Information and the Distillation of Stories

Dylan R. Ashley, J. Schmidhuber, Z. Friggstad, 2022, ArXiv.

Eliminating Meta Optimization Through Self-Referential Meta Learning

J. Schmidhuber, Louis Kirsch, 2022, ArXiv.

Self-Referential Meta Learning

J. Schmidhuber, Louis Kirsch, 2022 .

Adaptive decomposition of time

Jürgen Schmidhuber, J. Schmidhuber, 1991 .

Kernel representations for evolving continuous functions

J. Schmidhuber, J. Koutník, T. Glasmachers, 2012, Evolutionary Intelligence.

AutoIncSFA and vision-based developmental learning for humanoid robots

Jürgen Schmidhuber, Mikhail Frank, Jonathan Masci, 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

Draft : Deep Learning in Neural Networks : An Overview

J. Schmidhuber, 2014 .

On Learning Soccer Strategies

Jürgen Schmidhuber, Marco Wiering, Rafal Salustowicz, 1997, ICANN.

New Millennium AI and the Convergence of History: Update of 2012

Jürgen Schmidhuber, J. Schmidhuber, Jürgen Schmidhuber, 2012 .

Policy Gradient Critics

Jürgen Schmidhuber, Daan Wierstra, J. Schmidhuber, 2007, ECML.

Curiosity-driven optimization

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Artificial Scientists & Artists Based on the Formal Theory of Creativity

Jürgen Schmidhuber, J. Schmidhuber, 2010, AGI 2010.

Self-Motivated Development Through Rewards for Predictor Errors / Improvements

Jürgen Schmidhuber, J. Schmidhuber, 2005, AAAI 2005.

Exploring the predictable

Jürgen Schmidhuber, J. Schmidhuber, 2003 .

Sequential Decision Making Based on Direct Search

Jürgen Schmidhuber, J. Schmidhuber, 2001, Sequence Learning.

Learning Associative Inference Using Fast Weight Memory pdf

Jurgen Schmidhuber, Tsendsuren Munkhdalai, Imanol Schlag, 2020, ICLR.

The New AI: General & Sound & Relevant for Physics

Jürgen Schmidhuber, J. Schmidhuber, 2003, Artificial General Intelligence.

Gödel Machines: Towards a Technical Justification of Consciousness

Jürgen Schmidhuber, J. Schmidhuber, 2005, Adaptive Agents and Multi-Agent Systems.

On the Binding Problem in Artificial Neural Networks pdf

Klaus Greff, Sjoerd van Steenkiste, Jurgen Schmidhuber, 2020, ArXiv.

R-SQAIR: Relational Sequential Attend, Infer, Repeat pdf

Jürgen Schmidhuber, Aleksandar Stanic, J. Schmidhuber, 2019, ArXiv.

Unsupervised Object Keypoint Learning using Local Spatial Predictability

Sjoerd van Steenkiste, J. Schmidhuber, Anand Gopalakrishnan, 2020, ICLR.

Sequential Constant Size Compressors for Reinforcement Learning

Jürgen Schmidhuber, Matthew D. Luciw, Linus Gisslén, 2011, AGI.

Learning Unambiguous Reduced Sequence Descriptions

Jürgen Schmidhuber, J. Schmidhuber, 1991, NIPS.

A Neural Network Model for Inter-problem Adaptive Online Time Allocation

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2005, ICANN.

Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2010, NIPS.

A ‘Self-Referential’ Weight Matrix

Jürgen Schmidhuber, J. Schmidhuber, 1993 .

Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks

Jürgen Schmidhuber, J. Schmidhuber, 1992, Neural Computation.

Continually adding self-invented problems to the repertoire: First experiments with POWERPLAY

Jürgen Schmidhuber, Marijn F. Stollenga, Bas R. Steunebrink, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Self-Delimiting Neural Networks pdf

Jürgen Schmidhuber, J. Schmidhuber, 2012, ArXiv.

Philosophers & Futurists, Catch Up! Response to The Singularity

Jürgen Schmidhuber, J. Schmidhuber, 2012 .

The Fastest Way of Computing All Universes

Jürgen Schmidhuber, J. Schmidhuber, 2012 .

Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement

Jürgen Schmidhuber, Jieyu Zhao, Marco Wiering, 1997, Machine Learning.

1 Basic Ideas and Concepts : Overview

J. Schmidhuber, 2002 .

Reinforcement Learning with Self-Modifying Policies

Jürgen Schmidhuber, Jieyu Zhao, Nicol N. Schraudolph, 1998, Learning to Learn.

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Luca Maria Gambardella, Bogdan J. Matuszewski, Max A. Viergever, 2014, Medical Image Anal..

Are Disentangled Representations Helpful for Abstract Visual Reasoning?

Sjoerd van Steenkiste, Jurgen Schmidhuber, Francesco Locatello, 2019, NeurIPS.

Continual curiosity-driven skill acquisition from high-dimensional video inputs for humanoid robots

Marijn F. Stollenga, Varun Raj Kompella, Matthew D. Luciw, 2017, Artif. Intell..

Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis

Jürgen Schmidhuber, Marijn F. Stollenga, Varun Raj Kompella, 2016, Neural Computation.

Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams

Jürgen Schmidhuber, Varun Raj Kompella, Matthew D. Luciw, 2012, Neural Computation.

Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams pdf

Jürgen Schmidhuber, Varun Raj Kompella, Matthew D. Luciw, 2011, ArXiv.

Unsupervised Learning in LSTM Recurrent Neural Networks

Jürgen Schmidhuber, Nicol N. Schraudolph, Magdalena Klapper-Rybicka, 2001, ICANN.

Unsupervised Learning in Recurrent Neural Networks

Jürgen Schmidhuber, Nicol N. Schraudolph, Magdalena Klapper-Rybicka, 2001 .

Nonlinear ICA through low-complexity autoencoders

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

Going Beyond Linear Transformers with Recurrent Fast Weight Programmers pdf

Kazuki Irie, Imanol Schlag, J. Schmidhuber, 2021, NeurIPS.

Investigating object compositionality in Generative Adversarial Networks

Sjoerd van Steenkiste, J. Schmidhuber, S. Gelly, 2018, Neural Networks.

An Anti-hebbian Learning Rule to Represent Drive Motivations for Reinforcement Learning

Jürgen Schmidhuber, Varun Raj Kompella, Sohrob Kazerounian, 2014, SAB.

An intrinsic value system for developing multiple invariant representations with incremental slowness learning

Jürgen Schmidhuber, Varun Raj Kompella, Matthew D. Luciw, 2013, Front. Neurorobot..

Incremental Slow Feature Analysis

Jürgen Schmidhuber, Varun Raj Kompella, Matthew D. Luciw, 2011, IJCAI.

Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction

Jürgen Schmidhuber, Jonathan Masci, Ueli Meier, 2011, ICANN.

Recurrent World Models Facilitate Policy Evolution

Jürgen Schmidhuber, David Ha, J. Schmidhuber, 2018, NeurIPS.

Autonomous Acquisition of Natural Situated Communication

Jürgen Schmidhuber, Antonio Chella, Giovanni Pezzulo, 2014 .

Anytime Bounded Rationality

Jürgen Schmidhuber, Kristinn R. Thórisson, Bas R. Steunebrink, 2015, AGI.

Learning Restart Strategies

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2007, IJCAI.

Impact of Censored Sampling on the Performance of Restart Strategies

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2006, CP.

Dynamic Algorithm Portfolios

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2006, AI&M.

Adaptive Online Time Allocation to Search Algorithms

Jürgen Schmidhuber, Matteo Gagliolo, Viktor Zhumatiy, 2004, ECML.

Multi-Agent Learning with the Success-Story Algorithm

Jürgen Schmidhuber, Jieyu Zhao, J. Schmidhuber, 1996, ECAI Workshop LDAIS / ICMAS Workshop LIOME.

Autonomous reinforcement of behavioral sequences in neural dynamics

Jürgen Schmidhuber, Gregor Schöner, Yulia Sandamirskaya, 2012, The 2013 International Joint Conference on Neural Networks (IJCNN).

Artificial curiosity with planning for autonomous perceptual and cognitive development

Jurgen Schmidhuber, Matthew Luciw, Mark Ring, 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

The Modular Behavioral Environment for Humanoids and other Robots (MoBeE)

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2012, ICINCO.

Phoneme recognition in TIMIT with BLSTM-CTC pdf

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2008, ArXiv.

A Perspective on Objects and Systematic Generalization in Model-Based RL pdf

Klaus Greff, Sjoerd van Steenkiste, Jurgen Schmidhuber, 2019, ArXiv.

Binding via Reconstruction Clustering pdf

Jürgen Schmidhuber, Klaus Greff, Rupesh Kumar Srivastava, 2015, ArXiv.

VIA R ECONSTRUCTION C LUSTERING

J. Schmidhuber, R. Srivastava, Klaus Greff, 2015 .

Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks pdf

Sjoerd van Steenkiste, J. Schmidhuber, Jurgen Schmidhuber, 2020, ICLR.

Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning Rules

J. Schmidhuber, Kazuki Irie, 2022, ICLR.

Linear Transformers Are Secretly Fast Weight Programmers

Kazuki Irie, Imanol Schlag, J. Schmidhuber, 2021, ICML.

The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers

J. Schmidhuber, Kazuki Irie, R. Csordás, 2021, EMNLP.

Multi-dimensional Recurrent Neural Networks pdf

Jürgen Schmidhuber, Alex Graves, Santiago Fernández, 2007, ICANN.

Reinforcement Learning in Sparse-Reward Environments With Hindsight Policy Gradients

Jürgen Schmidhuber, Filipe Wall Mutz, Avinash Ummadisingu, 2021, Neural Computation.

Modular deep belief networks that do not forget

Jürgen Schmidhuber, Faustino J. Gomez, Leo Pape, 2011, The 2011 International Joint Conference on Neural Networks.

Episodic Reinforcement Learning by Logistic Reward-Weighted Regression

Tom Schaul, Jürgen Schmidhuber, Jan Peters, 2008, ICANN.

Market-Based Reinforcement Learning in Partially Observable Worlds

Jürgen Schmidhuber, Marcus Hutter, Ivo Kwee, 2001, ICANN.

An on-line algorithm for dynamic reinforcement learning and planning in reactive environments

Jürgen Schmidhuber, J. Schmidhuber, 1990, 1990 IJCNN International Joint Conference on Neural Networks.

Learning Algorithms for Networks with Internal and External Feedback

Jürgen Schmidhuber, J. Schmidhuber, 1990 .

Reinforcement Learning with Interacting Continually Running Fully Recurrent Networks

Jürgen Schmidhuber, J. Schmidhuber, 1990 .

A Natural Evolution Strategy for Multi-objective Optimization

Tom Schaul, Jürgen Schmidhuber, Tobias Glasmachers, 2010, PPSN.

Evolving neural networks in compressed weight space

Jürgen Schmidhuber, Jan Koutník, Faustino J. Gomez, 2010, GECCO '10.

Efficient natural evolution strategies

Tom Schaul, Yi Sun, Jürgen Schmidhuber, 2009, GECCO.

Meta Learning Backpropagation And Improving It

J. Schmidhuber, Louis Kirsch, 2020, NeurIPS.

The new AI is general and mathematically rigorous

Jürgen Schmidhuber, J. Schmidhuber, 2010 .

Source Separation as a By-Product of Regularization

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1998, NIPS.

LOCOCODE versus PCA and ICA

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1998 .

Unsupervised Coding with LOCOCODE

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1997, ICANN.

Simplifying Neural Nets by Discovering Flat Minima

Jürgen Schmidhuber, Sepp Hochreiter, S. Hochreiter, 1994, NIPS.

Predictive Coding with Neural Nets: Application to Text Compression

Jürgen Schmidhuber, Stefan Heil, J. Schmidhuber, 1994, NIPS.

Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets

Dylan R. Ashley, J. Schmidhuber, R. Srivastava, 2022, ArXiv.

Robust Texture Recognition Using Credal Classifiers

Jürgen Schmidhuber, Alessandro Giusti, Davide Migliore, 2010, BMVC.

Humanoid learns to detect its own hands

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2013, 2013 IEEE Congress on Evolutionary Computation.

Kernel representations for evolving continuous functions

Jürgen Schmidhuber, Jan Koutník, Tobias Glasmachers, 2012, Evol. Intell..

Transferring spatial perception between robots operating in a shared workspace

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving pdf

Jianfeng Gao, Jurgen Schmidhuber, Paul Smolensky, 2019, ArXiv.

Approaches to the Representation of Knowledge

Jürgen Schmidhuber, A. Stolcke, Wilfried Brauer, 1990 .

Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition

Jürgen Schmidhuber, Michael Wand, J. Schmidhuber, 2016, INTERSPEECH.

Recurrent nets that time and count

Jürgen Schmidhuber, Felix A. Gers, J. Schmidhuber, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A reinforcement learning approach for individualizing erythropoietin dosages in hemodialysis patients

Jürgen Schmidhuber, José David Martín-Guerrero, Emilio Soria-Olivas, 2009, Expert Syst. Appl..

Simple Algorithmic Principles of Discovery, Subjective Beauty, Selective Attention, Curiosity and Creativity

Jürgen Schmidhuber, J. Schmidhuber, 2007, ALT.

A Formal Theory of Creativity to Model the Creation of Art

Jürgen Schmidhuber, J. Schmidhuber, 2012 .

Driven by Compression Progress

Jürgen Schmidhuber, J. Schmidhuber, 2008, KES.

Accelerating Neural Self-Improvement via Bootstrapping

J. Schmidhuber, Kazuki Irie, 2023, ArXiv.

Large Language Model Programs

J. Schmidhuber, J. Weston, Sainbayar Sukhbaatar, 2023, ArXiv.

Domain-Adversarial Training for Session Independent EMG-based Speech Recognition

Tanja Schultz, Jürgen Schmidhuber, Michael Wand, 2018, INTERSPEECH.

Evolution of National Nobel Prize Shares in the 20th Century pdf

Jürgen Schmidhuber, J. Schmidhuber, 2010, ArXiv.

A Family of Gödel Machine Implementations

Jürgen Schmidhuber, Bas R. Steunebrink, J. Schmidhuber, 2011, AGI.

Contrastive Training of Complex-Valued Autoencoders for Object Discovery

J. Schmidhuber, Kazuki Irie, Anand Gopalakrishnan, 2023, ArXiv.

Multi-scale pyramidal pooling network for generic steel defect classification

Jürgen Schmidhuber, Jonathan Masci, Gabriel Fricout, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Application of mismatched Cellular Nonlinear Networks for Physical Cryptography

W. Porod, P. Lugli, Qingqing Chen, 2010, 2010 12th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA 2010).

On-Chip Electric Waves: An Analog Circuit Approach to Physical Uncloneable Functions

Jürgen Schmidhuber, Ulrich Rührmair, Ulf Schlichtmann, 2009, IACR Cryptol. ePrint Arch..

Modeling attacks on physical unclonable functions

Srinivas Devadas, Frank Sehnke, Jan Sölter, 2010, CCS '10.

Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots

Jürgen Schmidhuber, Alexander Förster, Matthew Luciw, 2013, Front. Psychol..

Resource-Bounded Machines are Motivated to be Effective, Efficient, and Curious

Jürgen Schmidhuber, Kristinn R. Thórisson, Jan Koutník, 2013, AGI.

Bias-Optimal Incremental Problem Solving

Jürgen Schmidhuber, J. Schmidhuber, 2002, NIPS.

PUF Modeling Attacks on Simulated and Silicon Data

Srinivas Devadas, Frank Sehnke, Jan Sölter, 2013, IEEE Transactions on Information Forensics and Security.

Learning the Long-Term Structure of the Blues

Jürgen Schmidhuber, Douglas Eck, J. Schmidhuber, 2002, ICANN.

DeepScores-A Dataset for Segmentation, Detection and Classification of Tiny Objects

Jürgen Schmidhuber, Marcello Pelillo, Ismail Elezi, 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

Recurrent Neural Network

Jürgen Schmidhuber, Zihang Dai, J. Schmidhuber, 2015 .

Improving Speaker-Independent Lipreading with Domain-Adversarial Training

Jürgen Schmidhuber, Michael Wand, J. Schmidhuber, 2017, INTERSPEECH.

Fusion Architectures for Word-Based Audiovisual Speech Recognition

Michael Wand, Jürgen Schmidhuber, J. Schmidhuber, 2020, INTERSPEECH.

Facial Expression Recognition with Recurrent Neural Networks

Jürgen Schmidhuber, Alex Graves, Bernd Radig, 2008 .

Efficient model-based exploration

Jürgen Schmidhuber, Marco Wiering, J. Schmidhuber, 1998 .

Learning tactile skills through curious exploration

Jürgen Schmidhuber, Christian Cipriani, Alexander Förster, 2012, Front. Neurorobot..

Bayesian Brains and the Rényi Divergence

Lancelot Da Costa, J. Schmidhuber, Thomas Parr, 2021, Neural Computation.

Continual Prediction using LSTM with Forget Gates

Jürgen Schmidhuber, Fred Cummins, Felix A. Gers, 1999 .

Prototype Resilient, Self-Modeling Robots

Jürgen Schmidhuber, J. Schmidhuber, 2007, Science.

Co-evolving recurrent neurons learn deep memory POMDPs

Jürgen Schmidhuber, Faustino J. Gomez, J. Schmidhuber, 2005, GECCO '05.

A neural network that embeds its own meta-levels

J. Schmidhuber, J. Schmidhuber, 1993, IEEE International Conference on Neural Networks.

Algorithm portfolio selection as a bandit problem with unbounded losses

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2011, Annals of Mathematics and Artificial Intelligence.

Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control pdf

Jürgen Schmidhuber, Róbert Csordás, J. Schmidhuber, 2019, ICLR.

A Novel Connectionist System for Unconstrained Handwriting Recognition

Edmondo Trentin, J. Schmidhuber, A. Graves, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Learning Framework for Morphological Operators Using Counter-Harmonic Mean

Jürgen Schmidhuber, Jesús Angulo, Jonathan Masci, 2012, ISMM.

Curious model-building control systems

Jürgen Schmidhuber, J. Schmidhuber, J. Schmidhuber, 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

Framewise phoneme classification with bidirectional LSTM networks

J. Schmidhuber, A. Graves, J. Schmidhuber, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

GATED FAST WEIGHTS FOR ASSOCIATIVE RETRIEVAL

Jürgen Schmidhuber, Imanol Schlag, J. Schmidhuber, 2018 .

Gated Fast Weights for On-The-Fly Neural Program Generation

Jürgen Schmidhuber, Imanol Schlag, J. Schmidhuber, 2017 .

Recurrent World Models Facilitate Policy Evolution

J. Schmidhuber, David R Ha, 2018, NeurIPS.

Adaptation of an EMG-Based Speech Recognizer via Meta-Learning

Tanja Schultz, Michael Wand, Jürgen Schmidhuber, 2019, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Object Recognition with Multi-Scale Pyramidal Pooling Networks pdf

Jürgen Schmidhuber, Jonathan Masci, Gabriel Fricout, 2012, ArXiv.

Linear Transformers Are Secretly Fast Weight Memory Systems pdf

Kazuki Irie, Imanol Schlag, J. Schmidhuber, 2021, ArXiv.

The DeepScoresV2 Dataset and Benchmark for Music Object Detection

Alexander Pacha, Lukas Tuggener, Thilo Stadelmann, 2020, 2020 25th International Conference on Pattern Recognition (ICPR).

Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and LSTM

Jürgen Schmidhuber, Douglas Eck, Felix A. Gers, 2002, Neural Computation.

Improving Baselines in the Wild pdf

Kazuki Irie, Imanol Schlag, J. Schmidhuber, 2021, ArXiv.

Novel Kernel-Based Recognizers of Human Actions

Jürgen Schmidhuber, Alessandro Giusti, Somayeh Danafar, 2010, EURASIP J. Adv. Signal Process..

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL

Dylan R. Ashley, J. Schmidhuber, R. Srivastava, 2022, ArXiv.

T-Learning pdf

Faustino J. Gomez, Vincent Graziano, Mark B. Ring, 2011, ArXiv.

Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization

Bram Bakker, J. Schmidhuber, B. Bakker, 2003 .

Long Short-Term Memory

S. Hochreiter, J. Schmidhuber, Sepp Hochreiter, 1997, Neural Computation.

Learning to Identify Critical States for Reinforcement Learning from Videos

J. Schmidhuber, Francesco Faccio, Bernard Ghanem, 2023, ArXiv.

Exploring the Promise and Limits of Real-Time Recurrent Learning

J. Schmidhuber, Kazuki Irie, Anand Gopalakrishnan, 2023, ArXiv.

On Narrative Information and the Distillation of Stories

Dylan R. Ashley, J. Schmidhuber, Z. Friggstad, 2022, arXiv.org.

Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits

J. Schmidhuber, A. Ramesh, Michelangelo Conserva, 2022, Neural Computation.

Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters

Jürgen Schmidhuber, Juan Antonio Pérez-Ortiz, Douglas Eck, 2002, ICANN.

Guiding Online Reinforcement Learning with Action-Free Offline Pretraining

J. Schmidhuber, Mohamed Elhoseiny, Deyao Zhu, 2023, ArXiv.

A scalable neural network architecture for board games

Tom Schaul, Jürgen Schmidhuber, J. Schmidhuber, 2008, 2008 IEEE Symposium On Computational Intelligence and Games.

Training and Generating Neural Networks in Compressed Weight Space pdf

Kazuki Irie, J. Schmidhuber, Jurgen Schmidhuber, 2021, ArXiv.

A Python Experiment Suite

Jürgen Schmidhuber, Thomas Rückstiess, J. Schmidhuber, 2011 .

Descriptor Learning for Omnidirectional Image Matching

Jürgen Schmidhuber, Jonathan Masci, Michael M. Bronstein, 2011, Registration and Recognition in Images and Videos.

Gradient-based Reinforcement Planning in Policy-Search Methods

Jürgen Schmidhuber, Marcus Hutter, Ivo Kwee, 2001, ArXiv.

Reward-Weighted Regression Converges to a Global Optimum

Dylan R. Ashley, J. Schmidhuber, R. Srivastava, 2021, AAAI.

Explore to see, learn to perceive, get the actions for free: SKILLABILITY

Jürgen Schmidhuber, Marijn F. Stollenga, Varun Raj Kompella, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Autonomous learning of abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis

Jürgen Schmidhuber, Marijn F. Stollenga, Varun Raj Kompella, 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

Hierarchical controller learning in a First-Person Shooter

Julian Togelius, Jürgen Schmidhuber, Niels van Hoorn, 2009, 2009 IEEE Symposium on Computational Intelligence and Games.

Reinforcement and shaping in learning action sequences with neural dynamics

Jürgen Schmidhuber, Gregor Schöner, Yulia Sandamirskaya, 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.

Reinforcement-Driven Shaping of Sequence Learning in Neural Dynamics

Jürgen Schmidhuber, Gregor Schöner, Yulia Sandamirskaya, 2014, SAB.

Recurrent Neural-Linear Posterior Sampling for Nonstationary Contextual Bandits pdf

Jurgen Schmidhuber, Aditya Ramesh, Paulo Rauber, 2020, Neural Computation.

Reinforcement Learning Soccer Teams with Incomplete World Models

Jürgen Schmidhuber, Marco Wiering, Rafal Salustowicz, 1999, Auton. Robots.

A Wavelet-based Encoding for Neuroevolution

Jürgen Schmidhuber, Kurt Driessens, Jan Koutník, 2016, GECCO.

On Fast Deep Nets for AGI Vision

Jürgen Schmidhuber, Alex Graves, Jonathan Masci, 2011, AGI.

Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals

Jürgen Schmidhuber, Alexander Förster, Matthew Luciw, 2014 .

Reflexive Collision Response with Virtual Skin - Roadmap Planning Meets Reinforcement Learning

Jürgen Schmidhuber, Mikhail Frank, Alexander Förster, 2016, ICAART.

Bounded Recursive Self-Improvement pdf

Jürgen Schmidhuber, Kristinn R. Thórisson, Antonio Chella, 2013, ArXiv.

A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks

Marcus Liwicki, Horst Bunke, Jürgen Schmidhuber, 2007 .

Hierarchical Relational Inference pdf

Sjoerd van Steenkiste, Jurgen Schmidhuber, Aleksandar Stani'c, 2020, AAAI.

Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot pdf

Jürgen Schmidhuber, Marcus Hutter, Faustino J. Gomez, 2006, IAS.

Robot Learning with State-Dependent Exploration

Frank Sehnke, Jürgen Schmidhuber, Martin Felder, 2008 .

Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks

J. Schmidhuber, F. Gomez, Santiago Fernández, 2006, ICML.

Efficient Interactive Multiclass Learning from Binary Feedback

Jürgen Schmidhuber, Alexander Förster, Matthew D. Luciw, 2014, TIIS.

Upper Confidence Weighted Learning for Efficient Exploration in Multiclass Prediction with Binary Feedback

Jürgen Schmidhuber, Matthew D. Luciw, Ngo Anh Vien, 2013, IJCAI.

World Models

J. Schmidhuber, David R Ha, 2018, ArXiv.

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

J. Schmidhuber, A. Giusti, D. Ciresan, 2013, MICCAI.

Incremental Basis Construction from Temporal Difference Error

Yi Sun, Jürgen Schmidhuber, Faustino J. Gomez, 2011, ICML.

An EM Based Training Algorithm for Recurrent Neural Networks

Yi Sun, Jürgen Schmidhuber, Jan Unkelbach, 2009, ICANN.

Algorithm Selection as a Bandit Problem with Unbounded Losses

Jürgen Schmidhuber, Matteo Gagliolo, J. Schmidhuber, 2008, LION.

Ontogenetic and Phylogenetic Reinforcement Learning

Julian Togelius, Tom Schaul, Jürgen Schmidhuber, 2009, Künstliche Intell..

Sequence learning through PIPE and automatic task decomposition

Jürgen Schmidhuber, Rafal Salustowicz, J. Schmidhuber, 1999 .

CMAC models learn to play soccer

Jürgen Schmidhuber, Marco Wiering, Rafal Salustowicz, 1998 .

From probabilities to programs with probabilistic incremental program evolution

Jürgen Schmidhuber, Rafal Salustowicz, J. Schmidhuber, 1999 .

Evolving Soccer Strategies

Jürgen Schmidhuber, Marco Wiering, Rafal Salustowicz, 1997, ICONIP.

Learning to Identify Critical States for Reinforcement Learning from Videos

J. Schmidhuber, Francesco Faccio, Bernard Ghanem, 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV).

The Benefits of Model-Based Generalization in Reinforcement Learning

J. Schmidhuber, A. Ramesh, Louis Kirsch, 2022, ICML.

Improving robot vision models for object detection through interaction

Jürgen Leitner, Jürgen Schmidhuber, Alexander Förster, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Learning visual object detection and localisation using icVision

Jürgen Leitner, Jürgen Schmidhuber, Mikhail Frank, 2013, BICA 2013.

Learning spatial object localization from vision on a humanoid robot

Mikhail Frank, Alexander Förster, Jürgen Leitner, 2012 .

An Application of Recurrent Neural Networks to Discriminative Keyword Spotting

J. Schmidhuber, Santiago Fernández, Alex Graves, 2007, ICANN.

2005 Special Issue: Framewise phoneme classification with bidirectional LSTM and other neural network architectures

J. Schmidhuber, Alex Graves, 2005 .

Evolving Modular Fast-Weight Networks for Control

Jürgen Schmidhuber, Faustino J. Gomez, J. Schmidhuber, 2005, ICANN.

Characteristic Kernels on Structured Domains Excel in Robotics and Human Action Recognition

Jürgen Schmidhuber, Arthur Gretton, Somayeh Danafar, 2010, ECML/PKDD.

2010 Special Issue: Parameter-exploring policy gradients

J. Schmidhuber, Jan Peters, Thomas Rückstieß, 2010 .

Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes

J. Schmidhuber, 2008, ABiALS.

New Millennium AI and the Convergence of History

J. Schmidhuber, 2006, Challenges for Computational Intelligence.

Reagent prediction with a molecular transformer improves reaction data quality

J. Schmidhuber, Djork-Arné Clevert, Michael Wand, 2023, Chemical science.

Is it enough to optimize CNN architectures on ImageNet?

J. Schmidhuber, Thilo Stadelmann, Lukas Tuggener, 2021, Frontiers in Computer Science.

Topological Neural Discrete Representation Learning à la Kohonen

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2023, ArXiv.

My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 pdf

Jürgen Schmidhuber, J. Schmidhuber, 2013, ArXiv.

Optimal Control Using the Transport Equation: The Liouville Machine

Jürgen Schmidhuber, Ivo Kwee, Ivo W. Kwee, 2001, Adapt. Behav..

Training and Generating Neural Networks in Compressed Weight Space

J. Schmidhuber, Kazuki Irie, 2021, ArXiv.

Approximating Two-Layer Feedforward Networks for Efficient Transformers

J. Schmidhuber, Kazuki Irie, R'obert Csord'as, 2023, EMNLP.

Assessment of algorithms for mitosis detection in breast cancer histopathology images

Jacob S. Vestergaard, J. Schmidhuber, A. Giusti, 2014, Medical Image Anal..

Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks

J. Schmidhuber, A. Graves, Alex Graves, 2008, NIPS 2008.

The Computational Universe

J. Schmidhuber, 2006 .