Thomas George Thuruthel
Biomedical Engineering · 2018–2020
A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Biomedical Engineering — 72 citations into it
- Mechanical Engineering — 28 citations into it
- Control and Systems Engineering — 24 citations into it
- Artificial Intelligence — 22 citations into it
- Reinforcement Learning — 19 citations into it
- Condensed Matter Physics — 18 citations into it
- Ocean Engineering — 6 citations into it
Whom Thomas George Thuruthel trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Diederik P. Kingma
- Bryan A. Jones
- George M. Whitesides
- Sepp Hochreiter
- Pieter Abbeel
- Marc Peter Deisenroth
- Geoffrey E. Hinton
- J. Urgen Schmidhuber
- Jimmy Ba
Papers from Thomas George Thuruthel's perspective
Ordered by how strongly the researchers Thomas George Thuruthel trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Soft robot perception using embedded soft sensors and recurrent neural networks (2019) — 595 citations
- Electronic skins and machine learning for intelligent soft robots (2020) — 642 citations
- Control Strategies for Soft Robotic Manipulators: A Survey (2018) — 588 citations
- Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators (2019) — 366 citations