Akshay Raj Dhamija

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 458 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 109 citations into it; #12,355 of 21,575 by trust here, #10,334 by citations
• Artificial Intelligence — 102 citations into it; #16,352 of 35,345 by trust here, #13,353 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Meta & Continual Learning — 23 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Akshay Raj Dhamija trusts overall

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

1. Ross B. Girshick
2. Alex Krizhevsky
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Shaoqing Ren
8. Diederik P. Kingma
9. Ilya Sutskever
10. Tsung-Yi Lin

Who trusts Akshay Raj Dhamija overall

1. Vikash Sehwag
2. Zhen Fang
3. Chuanxing Geng
4. Martin Mundt
5. Guangyao Chen
6. Yen-Chang Hsu
7. Ryne Roady
8. Jingkang Yang
9. K. J. Joseph
10. David Macêdo

Papers from Akshay Raj Dhamija's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• The Overlooked Elephant of Object Detection: Open Set (2020) — 199 citations
• Reducing Network Agnostophobia (2018) — 383 citations
• Self-Supervised Features Improve Open-World Learning (2021) — 15 citations
• Learning and the Unknown: Surveying Steps toward Open World Recognition (2019) — 159 citations