H-Index & Metrics Top Publications
Jost Tobias Springenberg

Jost Tobias Springenberg

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 31 Citations 13,082 50 World Ranking 7863 National Ranking 386

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

His primary areas of study are Artificial intelligence, Convolutional neural network, Machine learning, Pattern recognition and Deep learning. His Artificial intelligence study integrates concerns from other disciplines, such as Deconvolution and State. His Machine learning research is multidisciplinary, incorporating elements of Representation, Similarity and Training set.

In Pattern recognition, he works on issues like Generative model, which are connected to Image and Optimal control. The study incorporates disciplines such as Artificial neural network, Visualization and Decoding methods in addition to Deep learning. Jost Tobias Springenberg has included themes like Unsupervised learning, Discriminative model and Feature learning in his Semi-supervised learning study.

His most cited work include:

  • Striving for Simplicity: The All Convolutional Net (1388 citations)
  • Striving for Simplicity: The All Convolutional Net (848 citations)
  • Deep learning with convolutional neural networks for EEG decoding and visualization. (628 citations)

What are the main themes of his work throughout his whole career to date?

Jost Tobias Springenberg focuses on Artificial intelligence, Reinforcement learning, Machine learning, Convolutional neural network and Robot. As a member of one scientific family, Jost Tobias Springenberg mostly works in the field of Artificial intelligence, focusing on Pattern recognition and, on occasion, Generative model. His Reinforcement learning research is multidisciplinary, incorporating perspectives in Control, Robotics, Mathematical optimization and Hyperparameter.

His study in Machine learning is interdisciplinary in nature, drawing from both Training set and Robustness. His work carried out in the field of Convolutional neural network brings together such families of science as Range, Representation and State. His studies in Cognitive neuroscience of visual object recognition integrate themes in fields like Deconvolution and Convolution.

He most often published in these fields:

  • Artificial intelligence (76.71%)
  • Reinforcement learning (41.10%)
  • Machine learning (36.99%)

What were the highlights of his more recent work (between 2019-2021)?

  • Reinforcement learning (41.10%)
  • Artificial intelligence (76.71%)
  • Control (12.33%)

In recent papers he was focusing on the following fields of study:

Jost Tobias Springenberg mostly deals with Reinforcement learning, Artificial intelligence, Control, Machine learning and Mathematical optimization. His biological study spans a wide range of topics, including Robotic hand, Lift and Robotic arm. Jost Tobias Springenberg is studying Range, which is a component of Artificial intelligence.

His Control study integrates concerns from other disciplines, such as Tree, Computation and Local search. His Machine learning research incorporates themes from Robot, State and Benchmark. The Solver, Heuristics and Optimal control research Jost Tobias Springenberg does as part of his general Mathematical optimization study is frequently linked to other disciplines of science, such as Contraction operator, therefore creating a link between diverse domains of science.

Between 2019 and 2021, his most popular works were:

  • Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning (26 citations)
  • Critic Regularized Regression (23 citations)
  • Keep Doing What Worked: Behavior Modelling Priors for Offline Reinforcement Learning (15 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Jost Tobias Springenberg mainly investigates Reinforcement learning, Machine learning, Artificial intelligence, Robot and Variety. His Reinforcement learning study combines topics from a wide range of disciplines, such as Range and Entropy. Jost Tobias Springenberg has researched Range in several fields, including Margin, State and Benchmark.

His Entropy research integrates issues from Pixel, Mathematical optimization and Hyperparameter. The concepts of his Robot study are interwoven with issues in Control and Prior probability. Jost Tobias Springenberg integrates several fields in his works, including Multi-task learning and Weighting.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Striving for Simplicity: The All Convolutional Net

Jost Tobias Springenberg;Alexey Dosovitskiy;Thomas Brox;Martin A. Riedmiller.
arXiv: Learning (2014)

1331 Citations

Auto-sklearn: Efficient and Robust Automated Machine Learning

Matthias Feurer;Aaron Klein;Katharina Eggensperger;Jost Tobias Springenberg.
Automated Machine Learning (2019)

1188 Citations

Efficient and robust automated machine learning

Matthias Feurer;Aaron Klein;Katharina Eggensperger;Jost Tobias Springenberg.
neural information processing systems (2015)

993 Citations

Deep learning with convolutional neural networks for EEG decoding and visualization.

Robin Tibor Schirrmeister;Jost Tobias Springenberg;Lukas Dominique Josef Fiederer;Martin Glasstetter.
Human Brain Mapping (2017)

607 Citations

Learning to generate chairs with convolutional neural networks

Alexey Dosovitskiy;Jost Tobias Springenberg;Thomas Brox.
computer vision and pattern recognition (2015)

584 Citations

Discriminative Unsupervised Feature Learning with Convolutional Neural Networks

Alexey Dosovitskiy;Jost Tobias Springenberg;Martin Riedmiller;Thomas Brox.
neural information processing systems (2014)

480 Citations

Embed to control: a locally Linear Latent dynamics model for control from raw images

Manuel Watter;Jost Tobias Springenberg;Joschka Boedecker;Martin Riedmiller.
neural information processing systems (2015)

458 Citations

Multimodal deep learning for robust RGB-D object recognition

Andreas Eitel;Jost Tobias Springenberg;Luciano Spinello;Martin Riedmiller.
intelligent robots and systems (2015)

426 Citations

Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves

Tobias Domhan;Jost Tobias Springenberg;Frank Hutter.
international conference on artificial intelligence (2015)

404 Citations

Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks

Alexey Dosovitskiy;Philipp Fischer;Jost Tobias Springenberg;Martin Riedmiller.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2016)

316 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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