Glen Berseth

Artificial Intelligence · with Levine · 2015–2024

A researcher in the Science corpus: 9 papers; trusts 217 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 186 citations into it; #6,794 of 35,345 by trust here, #8,283 by citations
• Reinforcement Learning — 135 citations into it; #700 of 2,242 by trust here, #819 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #10,760 of 21,575 by trust here, #7,249 by citations
• Control and Systems Engineering — 65 citations into it; #765 of 8,257 by trust here, #549 by citations
• Meta & Continual Learning — 36 citations into it
• Biomedical Engineering — 33 citations into it; #3,717 of 10,734 by trust here, #3,366 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Computational Theory and Mathematics — 23 citations into it

Whom Glen Berseth trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. J. Urgen Schmidhuber
2. Sergey Levine
3. Geoffrey E. Hinton
4. Richard S. Sutton
5. Pieter Abbeel
6. Chelsea Finn
7. Yoshua Bengio
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Glen Berseth overall

1. Xue Bin Peng
2. J. Urgen Schmidhuber
3. Sergey Levine
4. Emilio Parisotto
5. Pieter Abbeel
6. Jorge Armando Mendez Mendez
7. Ananye Agarwal
8. Michiel van de Panne
9. Josh Merel
10. Ruslan Salakhutdinov

Papers from Glen Berseth's perspective

Ordered by how strongly the researchers Glen Berseth trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DeepLoco (2017) — 444 citations
• Terrain-adaptive locomotion skills using deep reinforcement learning (2016) — 267 citations
• Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control (2018) — 60 citations
• DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset (2024) — 905 citations
• Dynamic terrain traversal skills using reinforcement learning (2015) — 75 citations
• Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots (2021) — 296 citations
• CoMPS: Continual Meta Policy Search (2021) — 20 citations
• SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments (2021) — 44 citations
• A CCELERATING O NLINE R EINFORCEMENT L EARNING VIA M ODEL -B ASED M ETA -L EARNING (2021)