Peter Ondruska

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 190 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #6,192 of 35,345 by trust here, #3,273 by citations
• Computer Vision and Pattern Recognition — 57 citations into it; #11,811 of 21,575 by trust here, #2,915 by citations
• Natural Language Processing — 34 citations into it; #3,186 of 5,688 by trust here, #995 by citations
• Model Architectures — 25 citations into it
• Automotive Engineering — 14 citations into it
• Aerospace Engineering — 13 citations into it; #1,320 of 6,421 by trust here, #2,997 by citations
• Reinforcement Learning — 12 citations into it; #1,412 of 2,242 by trust here, #1,415 by citations
• Representation & Self-Supervised Learning — 11 citations into it

Whom Peter Ondruska trusts overall

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

1. Yoshua Bengio
2. Ilya Sutskever
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Graves
8. Richard Socher
9. Kaiming He
10. Sepp Hochreiter

Who trusts Peter Ondruska overall

1. Ahmad El Sallab
2. Lei Tai
3. Pinxin Long
4. Mohammed Abdou
5. Tuan Anh Le
6. Fabian B. Fuchs
7. Seonghyeon Park
8. Sergey Levine
9. Rajarshee Mitra
10. Wenxi Liu

Papers from Peter Ondruska's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015) — 1,220 citations
• One Thousand and One Hours: Self-driving Motion Prediction Dataset (2020) — 483 citations
• Maximum Entropy Deep Inverse Reinforcement Learning (2015) — 458 citations
• Deep Tracking: Seeing Beyond Seeing Using Recurrent Neural Networks (2016) — 249 citations
• Deep tracking in the wild: End-to-end tracking using recurrent neural networks (2018) — 112 citations
• Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks (2016) — 20 citations