Bhaskara Marthi
Artificial Intelligence · 2002–2017
A researcher in the Science corpus: 9 papers; trusts 46 researchers and is trusted by 13 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 61 citations into it; #34,630 of 35,345 by trust here, #17,412 by citations
- Computer Vision and Pattern Recognition — 46 citations into it; #12,750 of 21,575 by trust here, #11,498 by citations
- Reinforcement Learning — 37 citations into it
- Control and Systems Engineering — 27 citations into it; #8,014 of 8,257 by trust here, #2,078 by citations
- Probabilistic & Bayesian Methods — 20 citations into it; #937 of 1,525 by trust here, #531 by citations
- Aerospace Engineering — 16 citations into it; #3,089 of 6,421 by trust here, #4,066 by citations
- Robotics & Control — 13 citations into it
- Management Science and Operations Research — 8 citations into it
Whom Bhaskara Marthi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald E. Parr
- Thomas G. Dietterich
- Carlos Guestrin
- Stuart J. Russell
- David Andre
- R. Fikes
- Richard S. Sutton
- Radford M. Neal
- Radu Bogdan Rusu
- Andrew Y. Ng
Who trusts Bhaskara Marthi overall
- Vaishak Belle
- Stuart J. Russell
- Jacob Andreas
- Eric Brochu
- Wenling Shang
- Gabriel Synnaeve
- David Wingate
- Daniel Klein
- Sergey Levine
- William W. Cohen
Papers from Bhaskara Marthi's perspective
Ordered by how strongly the researchers Bhaskara Marthi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Reinforcement Learning: An Introduction
Their papers
- A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs (2017) — 270 citations
- Learning grounded finite-state representations from unstructured demonstrations (2015) — 228 citations
- Incremental Semantically Grounded Learning from Demonstration (2013) — 135 citations
- Object disappearance for object discovery (2012) — 24 citations
- Unsupervised discovery of object classes with a mobile robot (2014) — 6 citations
- Autonomous door opening and plugging in with a personal robot (2010) — 203 citations
- Concurrent Hierarchical Reinforcement Learning (2005) — 109 citations
- BLOG: Probabilistic Models with Unknown Objects (2005) — 550 citations
- Identity Uncertainty and Citation Matching (2002) — 357 citations