Sanjiban Choudhury
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 6 papers; trusts 44 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 77 citations into it; #33,837 of 35,345 by trust here, #20,377 by citations
- Reinforcement Learning — 70 citations into it; #2,154 of 2,242 by trust here, #1,778 by citations
- Computer Vision and Pattern Recognition — 35 citations into it; #20,687 of 21,575 by trust here, #14,478 by citations
- Control and Systems Engineering — 26 citations into it; #7,871 of 8,257 by trust here, #3,865 by citations
- Management Science and Operations Research — 20 citations into it
- Optimization & Training — 11 citations into it
- Computational Theory and Mathematics — 8 citations into it
- Statistical and Nonlinear Physics — 6 citations into it
Whom Sanjiban Choudhury trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Stéphane Ross
- J. Bagnell
- David Silver
- Jan Peters
- Richard S. Sutton
- Geoffrey J. Gordon
- Geoffrey E. Hinton
- Sergey Levine
- Pieter Abbeel
- Diederik P. Kingma
Who trusts Sanjiban Choudhury overall
- Yuxi Li
- Aravind Rajeswaran
- Jeffrey Mahler
- Kendall Lowrey
- Dimitri P. Bertsekas
- Wen Sun
- Yunchang Zhu
- E. Todorov
- Ken Goldberg
- Frank S. He
Papers from Sanjiban Choudhury's perspective
Ordered by how strongly the researchers Sanjiban Choudhury 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- GENERATIVE ADVERSARIAL NETS
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Learning from Interventions: Human-robot interaction as both explicit and implicit feedback (2020) — 84 citations
- Imitation Learning as f-Divergence Minimization (2019) — 193 citations
- Posterior Sampling for Anytime Motion Planning on Graphs with Expensive-to-Evaluate Edges (2020) — 14 citations
- Adaptive Information Gathering via Imitation Learning (2017) — 24 citations
- Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap (2021) — 95 citations
- Approximate Dynamic Programming (2018) — 484 citations