Siddharth Srivastava
Artificial Intelligence · 2007–2022
A researcher in the Science corpus: 12 papers; trusts 117 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 183 citations into it; #12,810 of 35,345 by trust here, #6,422 by citations
- Computer Vision and Pattern Recognition — 166 citations into it; #17,045 of 21,575 by trust here, #6,206 by citations
- Reinforcement Learning — 128 citations into it; #1,713 of 2,242 by trust here, #2,063 by citations
- Control and Systems Engineering — 87 citations into it; #6,529 of 8,257 by trust here, #837 by citations
- Robotics & Control — 83 citations into it; #439 of 609 by trust here, #192 by citations
- Optimization & Training — 12 citations into it
- Management Science and Operations Research — 8 citations into it
- Sociology and Political Science — 8 citations into it; #19,788 of 22,756 by trust here, #6,886 by citations
Whom Siddharth Srivastava trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Andrew Barto
- Steven M. LaValle
- Geoffrey E. Hinton
- Diederik P. Kingma
- Andreas Podelski
- Alex Krizhevsky
- David Silver
- Ilya Sutskever
- Blai Bonet
Who trusts Siddharth Srivastava overall
- Marc Toussaint
- Hector Geffner
- Tom Silver
- Blai Bonet
- Suraj Nair
- Caelan Reed Garrett
- Sarath Sreedharan
- Rohan Chitnis
- Yuke Zhu
- Danny Driess
Papers from Siddharth Srivastava's perspective
Ordered by how strongly the researchers Siddharth Srivastava trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- 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)
Their papers
- Combined task and motion planning through an extensible planner-independent interface layer (2014) — 550 citations
- BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments (2021) — 255 citations
- Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55 (2017) — 70 citations
- iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks (2021) — 329 citations
- iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020) — 246 citations
- Learning Generalized Reactive Policies using Deep Neural Networks (2017) — 133 citations
- A new representation and associated algorithms for generalized planning (2011) — 101 citations
- Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations (2018) — 20 citations
- Anonymizing Social Networks (2007) — 1,071 citations
- BEHAVIOR-1K: A Benchmark for Embodied AI with 1, 000 Everyday Activities and Realistic Simulation (2022) — 370 citations
- iGibson, a Simulation Environment for Interactive Tasks in Large Realistic Scenes (2020) — 57 citations
- Markovian State and Action Abstractions for MDPs via Hierarchical MCTS (2016) — 65 citations