Springenberg, Jost Tobias

Deep learning with convolutional neural networks for EEG decoding and visualization

Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017) · Human Brain Mapping

EEG and Brain-Computer InterfacesEmotion and Mood Recognition

Cited by 3,751

Striving for Simplicity: The All Convolutional Net

Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 3,447

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 762

Learning to generate chairs with convolutional neural networks

Dosovitskiy, A., Springenberg, J. T., & Brox, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

Cited by 640

Graph networks as learnable physics engines for inference and control

Sanchez-Gonzalez, A., Heess, N., Springenberg, J. T., Merel, J., Riedmiller, M., Hadsell, R., & Battaglia, P. (2018) · arXiv

Reinforcement Learning in RoboticsModel Reduction and Neural Networks

Cited by 387

Maximum a Posteriori Policy Optimisation

Abdolmaleki, A., Springenberg, J. T., Tassa, Y., Munos, R., Heess, N., & Riedmiller, M. (2018) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 259

Critic regularized regression

Wang, Z., Novikov, A., Zolna, K., Springenberg, J. T., Reed, S., Shahriari, B., Siegel, N., Merel, J., Gulcehre, C., Heess, N., et al. (2020) · arXiv

Reinforcement Learning in RoboticsAdaptive Dynamic Programming Control

Cited by 128

A Generalist Agent

Reed, S., Zolna, K., Parisotto, E., Colmenarejo, S. G., Novikov, A., Barth-Maron, G., Gimenez, M., Sulsky, Y., Kay, J., Springenberg, J. T., et al. (2022) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 63

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Bousmalis, K., Vezzani, G., Rao, D., Devin, C., Lee, A. X., Bauza, M., Davchev, T., Zhou, Y., Gupta, A., Raju, A., et al. (2023) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 13

Gemini Robotics: Bringing AI into the Physical World

Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 8

$π_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Intelligence, P., Black, K., Brown, N., Darpinian, J., Dhabalia, K., Driess, D., Esmail, A., Equi, M., Finn, C., Fusai, N., et al. (2025) · arXiv

Robot Manipulation and LearningReinforcement Learning in Robotics

Cited by 3