Karl Schmeckpeper

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it
• Control and Systems Engineering — 46 citations into it
• Reinforcement Learning — 42 citations into it
• Computer Vision and Pattern Recognition — 39 citations into it
• Robotics & Control — 29 citations into it

Whom Karl Schmeckpeper trusts overall

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

1. Chelsea Finn
2. Sergey Levine
3. Alex Krizhevsky
4. Kaiming He
5. Geoffrey E. Hinton
6. Diederik P. Kingma
7. Pieter Abbeel
8. Yoshua Bengio
9. Yann LeCun
10. Ilya Sutskever

Papers from Karl Schmeckpeper's perspective

Ordered by how strongly the researchers Karl Schmeckpeper 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. Et al
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• RoboNet: Large-Scale Multi-Robot Learning (2019) — 437 citations
• Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets (2021) — 365 citations