Schmidhuber, Jürgen

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation

Neural Networks and ApplicationsHydrological Forecasting Using AI

Cited by 102,318

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks

Neural Networks and ApplicationsMachine Learning and Algorithms

Cited by 18,570

LSTM: A Search Space Odyssey

Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J. (2015) · arXiv

Neural Networks and ApplicationsSpeech Recognition and Synthesis

Cited by 7,073

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

Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 5,370

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science

AI in cancer detectionCell Image Analysis Techniques

Cited by 1,536

Multi-column deep neural network for traffic sign classification

Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks

Advanced Neural Network ApplicationsComputer Science and Engineering

Cited by 1,057

Bidirectional LSTM Networks for Improved Phoneme Classification and Recognition

Graves, A., Fernández, S., & Schmidhuber, J. (2005) · Lecture Notes in Computer Science

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 925

Highway Networks

Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 912

Formal theory of creativity, fun, and intrinsic motivation (1990–2010)

Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development

Reinforcement Learning in RoboticsPsychiatry, Mental Health, Neuroscience

Cited by 747

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

Schmidhuber, J. (1992) · Neural Computation

Neural Networks and ApplicationsNeural Networks and Reservoir Computing

Cited by 357

Multi-dimensional Recurrent Neural Networks

Graves, A., Fernández, S., & Schmidhuber, J. (2007) · Lecture Notes in Computer Science

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 303

Probabilistic Incremental Program Evolution

Salustowicz, R., & Schmidhuber, J. (1997) · Evolutionary Computation

Evolutionary Algorithms and ApplicationsTeaching and Learning Programming

Cited by 249

World Models

Ha, D., & Schmidhuber, J. (2018) · Zenodo

Computer Graphics and Visualization TechniquesAdvanced Database Systems and Queries

Cited by 187

Planning to be surprised: optimal bayesian exploration in dynamic environments

Sun, Y., Gomez, F., & Schmidhuber, J. (2011) · Lecture notes in computer science

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 134

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

Koutník, J., Cuccu, G., Schmidhuber, J., & Gomez, F. (2013) · Proceedings of the 15th annual conference on Genetic and evolutionary computation

Evolutionary Algorithms and ApplicationsReinforcement Learning in Robotics

Cited by 130

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

Schmidhuber, J. (2007) · Cognitive Technologies

Computability, Logic, AI AlgorithmsLogic, programming, and type systems

Cited by 110

On the Binding Problem in Artificial Neural Networks

Greff, K., van Steenkiste, S., & Schmidhuber, J. (2020) · arXiv

Neural Networks and ApplicationsPsychiatry, Mental Health, Neuroscience

Cited by 102

Exploring the predictable

Schmidhuber, J. (2003) · Natural Computing Series

Evolutionary Algorithms and ApplicationsComputability, Logic, AI Algorithms

Cited by 65

Linear Transformers Are Secretly Fast Weight Programmers

Schlag, I., Irie, K., & Schmidhuber, J. (2021) · arXiv

Topic ModelingNeural Networks and Applications

Cited by 60

Learning tactile skills through curious exploration

Pape, L., Oddo, C. M., Controzzi, M., Cipriani, C., Förster, A., Carrozza, M. C., & Schmidhuber, J. (2012) · Frontiers in Neurorobotics

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 51

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Giusti, A., Cireşan, D. C., Masci, J., Gambardella, L. M., & Schmidhuber, J. (2013) · arXiv

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 48

Deep Big Multilayer Perceptrons for Digit Recognition

Cireşan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2012) · Lecture Notes in Computer Science

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 48

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

Schmidhuber, J. (1993) · ICANN ’93

Neural Networks and ApplicationsNeural Networks and Reservoir Computing

Cited by 34

Mindstorms in Natural Language-Based Societies of Mind

Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsMulti-Agent Systems and Negotiation

Cited by 24