Aditya Kanade
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 7 papers; trusts 158 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 114 citations into it; #19,997 of 35,345 by trust here, #15,236 by citations
- Natural Language Processing — 71 citations into it; #2,634 of 5,688 by trust here, #3,053 by citations
- Information Systems — 61 citations into it; #805 of 8,771 by trust here, #764 by citations
- Software — 46 citations into it; #257 of 1,227 by trust here, #123 by citations
- Code Generation & Program Synthesis — 29 citations into it
- Program Analysis & Verification — 24 citations into it; #43 of 301 by trust here, #14 by citations
- Computer Vision and Pattern Recognition — 22 citations into it
- Representation & Self-Supervised Learning — 19 citations into it; #2,098 of 3,145 by trust here, #1,754 by citations
Whom Aditya Kanade trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Miltiadis Allamanis
- Tomas Mikolov
- Sepp Hochreiter
- Geoffrey E. Hinton
- Ilya Sutskever
- Alec Radford
- Yoshua Bengio
- Jimmy Ba
- Alex Krizhevsky
Who trusts Aditya Kanade overall
- Michihiro Yasunaga
- Baptiste Rozière
- Daya Guo
- Percy Liang
- Kevin Jesse
- Erik Nijkamp
- Yichen Xu
- Vincent J. Hellendoorn
- Zimin Chen
- Disha Shrivastava
Papers from Aditya Kanade's perspective
Ordered by how strongly the researchers Aditya Kanade 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- A framework for the extraction of Deep Neural Networks by leveraging public data (2019) — 63 citations
- Neural Program Repair by Jointly Learning to Localize and Repair (2019) — 138 citations
- Deep Reinforcement Learning for Programming Language Correction (2018) — 34 citations
- Pre-trained Contextual Embedding of Source Code (2019) — 83 citations
- Neural Attribution for Semantic Bug-Localization in Student Programs (2019) — 41 citations
- Learning and Evaluating Contextual Embedding of Source Code (2019) — 452 citations
- DeepFix: Fixing Common C Language Errors by Deep Learning (2017) — 549 citations