Shariq Iqbal

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 7 papers; trusts 151 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #7,896 of 35,345 by trust here, #13,353 by citations
• Reinforcement Learning — 82 citations into it; #617 of 2,242 by trust here, #1,253 by citations
• Computer Vision and Pattern Recognition — 57 citations into it
• Optimization & Training — 25 citations into it
• Model Architectures — 21 citations into it
• Game & Decision Theory — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it; #2,010 of 12,182 by trust here, #2,851 by citations
• Representation & Self-Supervised Learning — 12 citations into it

Whom Shariq Iqbal trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Jakob N. Foerster
6. Jimmy Ba
7. Pieter Abbeel
8. Alex Krizhevsky
9. Alex Graves
10. Ronald J. Williams

Who trusts Shariq Iqbal overall

1. Tonghan Wang
2. Tabish Rashid
3. Jiechuan Jiang
4. Yuhang Zhao
5. Xiaoteng Ma
6. Kyunghwan Son
7. Bei Peng
8. Yihan Wang
9. Tianhao Zhang
10. Julien Roy

Papers from Shariq Iqbal's perspective

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

Their papers

• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Actor-Attention-Critic for Multi-Agent Reinforcement Learning (2018) — 970 citations
• Gemma 3 Technical Report (2025) — 1,621 citations
• Training Language Models to Self-Correct via Reinforcement Learning (2024) — 429 citations
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning (2020) — 88 citations
• AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning (2020) — 23 citations
• When MAML Can Adapt Fast and How to Assist When It Cannot (2019) — 31 citations