Ameya Prabhu

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 255 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #4,801 of 35,345 by trust here, #6,064 by citations
• Computer Vision and Pattern Recognition — 77 citations into it; #3,855 of 21,575 by trust here, #4,834 by citations
• Meta & Continual Learning — 56 citations into it; #102 of 1,041 by trust here, #144 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Optimization & Training — 12 citations into it
• Reinforcement Learning — 11 citations into it
• Efficiency & Compression — 6 citations into it
• Model Architectures — 5 citations into it

Whom Ameya Prabhu trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Arslan Chaudhry
7. Diederik P. Kingma
8. Rahaf Aljundi
9. Ross B. Girshick
10. Marc'Aurelio Ranzato

Who trusts Ameya Prabhu overall

1. Jiangpeng He
2. Yaoyao Liu
3. Bernt Schiele
4. Feng Zhu
5. Lucas Caccia
6. Rahaf Aljundi
7. Yiduo Guo
8. Zhipeng Cai
9. Wenxuan Zhang
10. Byeonghwi Kim

Papers from Ameya Prabhu's perspective

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

Their papers

• GDumb: A Simple Approach that Questions Our Progress in Continual Learning (2020) — 735 citations
• Inverse Scaling: When Bigger Isn't Better (2023) — 235 citations
• Computationally Budgeted Continual Learning: What Does Matter? (2023) — 90 citations