Rahul Gupta
Artificial Intelligence · with Kanade · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 30 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it; #26,242 of 35,345 by trust here, #30,347 by citations
- Information Systems — 19 citations into it; #1,263 of 8,771 by trust here, #1,203 by citations
- Software — 18 citations into it; #243 of 1,227 by trust here, #158 by citations
- Natural Language Processing — 13 citations into it; #3,657 of 5,688 by trust here, #4,983 by citations
- Program Analysis & Verification — 8 citations into it; #62 of 301 by trust here, #22 by citations
- Reinforcement Learning — 8 citations into it; #1,766 of 2,242 by trust here, #2,092 by citations
- Computer Science Applications — 7 citations into it
- Computer Vision and Pattern Recognition — 6 citations into it
Whom Rahul Gupta trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Jimmy Ba
- Ilya Sutskever
- Geoffrey E. Hinton
- Richard S. Sutton
- Sepp Hochreiter
- Rishabh Singh
- Oriol Vinyals
- Mark J van Rossum
- Yoshua Bengio
Who trusts Rahul Gupta overall
- Michihiro Yasunaga
- Percy Liang
- Michael Pradel
- Aditya Kanade
- Jacob A. Harer
- Rishabh Singh
- Hossein Hajipour
- Michele Tufano
- Koushik Sen
- Ke Wang
Papers from Rahul Gupta's perspective
Ordered by how strongly the researchers Rahul Gupta 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Deep Reinforcement Learning for Programming Language Correction (2018) — 34 citations
- Neural Attribution for Semantic Bug-Localization in Student Programs (2019) — 41 citations
- DeepFix: Fixing Common C Language Errors by Deep Learning (2017) — 549 citations