Ashish Kumar

Artificial Intelligence · with Malik · 2019–2022

A researcher in the Science corpus: 3 papers; trusts 117 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Reinforcement Learning — 49 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Biomedical Engineering — 38 citations into it; #9,940 of 10,734 by trust here, #5,712 by citations
• Control and Systems Engineering — 36 citations into it
• Aerospace Engineering — 19 citations into it
• Robotics & Control — 17 citations into it
• Optimization & Training — 9 citations into it

Whom Ashish Kumar trusts overall

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

1. Sergey Levine
2. Diederik P. Kingma
3. Jemin Hwangbo
4. Geoffrey E. Hinton
5. Pieter Abbeel
6. Alex Krizhevsky
7. Xue Bin Peng
8. Chelsea Finn
9. Yoshua Bengio
10. John Schulman

Who trusts Ashish Kumar overall

1. Gabriel B. Margolis
2. Zipeng Fu
3. Ananye Agarwal
4. Anurag Ajay
5. Ziwen Zhuang
6. Xuxin Cheng
7. Pulkit Agrawal
8. Aviral Kumar
9. Ge Yang
10. Tushar Nagarajan

Papers from Ashish Kumar's perspective

Ordered by how strongly the researchers Ashish Kumar 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• RMA: Rapid Motor Adaptation for Legged Robots (2021) — 938 citations
• Legged Locomotion in Challenging Terrains using Egocentric Vision (2022) — 335 citations
• Learning Navigation Subroutines from Egocentric Videos (2019) — 31 citations