Ali Malik

Artificial Intelligence · with Zhang · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 10 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it; #34,711 of 35,345 by trust here, #2,684 by citations
• Reinforcement Learning — 14 citations into it; #2,206 of 2,242 by trust here, #2,160 by citations

Whom Ali Malik trusts overall

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

1. Yarin Gal
2. Tilmann Gneiting
3. Richard S. Sutton
4. J. Platt
5. Suchi Saria
6. Zoubin Ghahramani
7. Geoffrey E. Hinton
8. E. Todorov
9. Volodymyr Kuleshov
10. Adrian E. Raftery

Who trusts Ali Malik overall

1. Michael Janner
2. Anusha Nagabandi
3. Sergey Levine
4. Qiyang Li

Papers from Ali Malik's perspective

Ordered by how strongly the researchers Ali Malik 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Reinforcement Learning: An Introduction
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• Calibrated Model-Based Deep Reinforcement Learning (2019) — 62 citations