Muhammad Abdullah Jamal

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 155 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #21,597 of 35,345 by trust here, #13,760 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Reinforcement Learning — 8 citations into it

Whom Muhammad Abdullah Jamal trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Chelsea Finn
10. J. Urgen Schmidhuber

Who trusts Muhammad Abdullah Jamal overall

1. Jun Shu
2. Janarthanan Rajendran
3. Chi Zhang
4. Chunbo Lang
5. Shiliang Sun
6. Guangyuan Shi
7. Yisheng Song
8. Huiping Zhuang
9. Mingzhang Yin
10. Chengyu Wang

Papers from Muhammad Abdullah Jamal's perspective

Ordered by how strongly the researchers Muhammad Abdullah Jamal 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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective (2020) — 305 citations
• Task Agnostic Meta-Learning for Few-Shot Learning (2018) — 539 citations