Prasoon Goyal

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 79 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #14,084 of 35,345 by trust here, #2,388 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #9,984 of 21,575 by trust here, #1,803 by citations
• Reinforcement Learning — 31 citations into it; #1,079 of 2,242 by trust here, #231 by citations
• Natural Language Processing — 29 citations into it; #3,867 of 5,688 by trust here, #3,638 by citations
• Control and Systems Engineering — 6 citations into it
• Optimization & Training — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Prasoon Goyal trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Andrew Y. Ng
4. Ruslan Salakhutdinov
5. Yoshua Bengio
6. Alex Krizhevsky
7. David M. Blei
8. Richard Socher
9. Karen Simonyan
10. Yann LeCun

Who trusts Prasoon Goyal overall

1. Andrew Kyle Lampinen
2. Jesse Mu
3. J'er'emy Scheurer
4. Suvir Mirchandani
5. J. Tomczak
6. Catherine Wong
7. Allison C. Tam
8. Nicholas A. Roy
9. Corey Lynch
10. Jelena Luketina

Papers from Prasoon Goyal's perspective

Ordered by how strongly the researchers Prasoon Goyal 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

• End to End Learning for Self-Driving Cars (2016) — 4,658 citations
• Using Natural Language for Reward Shaping in Reinforcement Learning (2019) — 215 citations
• Nonparametric Variational Auto-Encoders for Hierarchical Representation Learning (2017) — 123 citations
• PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards (2020) — 61 citations
• Transliterated Mobile Keyboard Input via Weighted Finite-State Transducers (2017) — 31 citations