Mohi Khansari

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 7 papers; trusts 321 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 160 citations into it; #11,153 of 35,345 by trust here, #6,468 by citations
• Computer Vision and Pattern Recognition — 116 citations into it
• Control and Systems Engineering — 108 citations into it; #2,032 of 8,257 by trust here, #351 by citations
• Reinforcement Learning — 108 citations into it; #973 of 2,242 by trust here, #614 by citations
• Robotics & Control — 44 citations into it; #254 of 609 by trust here, #238 by citations
• Biomedical Engineering — 30 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Mechanical Engineering — 14 citations into it

Whom Mohi Khansari trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Sergey Levine
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Chelsea Finn
8. Ilya Sutskever
9. Yann LeCun
10. Richard S. Sutton

Who trusts Mohi Khansari overall

1. Corey Lynch
2. Sergey Levine
3. Sudeep Dasari
4. Ajay Mandlekar
5. Pierre Sermanet
6. Benjamin Eysenbach
7. Suraj Nair
8. Ted Xiao
9. Abhishek Gupta
10. Dibya Ghosh

Papers from Mohi Khansari's perspective

Ordered by how strongly the researchers Mohi Khansari trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Latent Plans from Play (2019) — 512 citations
• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning (2022) — 813 citations
• RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer (2020) — 132 citations
• Modeling Long-horizon Tasks as Sequential Interaction Landscapes (2020) — 30 citations
• Watch, Try, Learn: Meta-Learning from Demonstrations and Reward (2019) — 54 citations
• Learning Grasping Interaction with Geometry-aware 3D Representations (2017) — 8 citations
• Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data (2020) — 26 citations