Rohan Chitnis
Artificial Intelligence · 2014–2022
A researcher in the Science corpus: 6 papers; trusts 108 researchers and is trusted by 18 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 109 citations into it; #8,433 of 35,345 by trust here, #10,430 by citations
- Reinforcement Learning — 66 citations into it
- Computer Vision and Pattern Recognition — 44 citations into it; #6,254 of 21,575 by trust here, #8,205 by citations
- Control and Systems Engineering — 30 citations into it; #5,835 of 8,257 by trust here, #2,004 by citations
- Natural Language Processing — 29 citations into it; #2,084 of 5,688 by trust here, #4,601 by citations
- Optimization & Training — 15 citations into it
- Robotics & Control — 15 citations into it; #328 of 609 by trust here, #311 by citations
- Biomedical Engineering — 12 citations into it
Whom Rohan Chitnis trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Sergey Levine
- Jimmy Ba
- John Schulman
- Ilya Sutskever
- Alex Krizhevsky
- Richard S. Sutton
- Karen Simonyan
Who trusts Rohan Chitnis overall
- Felix Stahlberg
- Jiameng Gao
- Tamali Banerjee
- Ivan Provilkov
- Eva Hasler
- Pushpak Bhattacharyya
- Taku Kudo
- Dmitrii Emelianenko
- Tom Silver
- Bill Byrne
Papers from Rohan Chitnis's perspective
Ordered by how strongly the researchers Rohan Chitnis 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Combined task and motion planning through an extensible planner-independent interface layer (2014) — 550 citations
- Integrated Task and Motion Planning (2020) — 718 citations
- Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning (2022) — 19 citations
- Variable-Length Word Encodings for Neural Translation Models (2015) — 41 citations
- Efficient Bimanual Manipulation Using Learned Task Schemas (2019) — 89 citations
- Intrinsic Motivation for Encouraging Synergistic Behavior (2020) — 28 citations