Vinod Kumar Kurmi

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 373 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 74 citations into it; #28,662 of 35,345 by trust here, #27,551 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #17,616 of 21,575 by trust here, #17,757 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Meta & Continual Learning — 17 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Generative Models — 9 citations into it
• Radiology, Nuclear Medicine and Imaging — 8 citations into it; #2,228 of 3,265 by trust here, #2,837 by citations
• Model Architectures — 7 citations into it

Whom Vinod Kumar Kurmi trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yarin Gal
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yann LeCun
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Vinod Kumar Kurmi overall

1. Badri N. Patro
2. Liyuan Wang
3. Haoxuan Qu
4. Vinay P. Namboodiri
5. Mayank Lunayach
6. Sentao Chen
7. Xingxing Zhang
8. Hui Tang
9. Hossein Rahmani

Papers from Vinod Kumar Kurmi's perspective

Ordered by how strongly the researchers Vinod Kumar Kurmi 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting (2021) — 26 citations
• Attending to Discriminative Certainty for Domain Adaptation (2019) — 113 citations