Riashat Islam

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 431 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 287 citations into it; #3,298 of 35,345 by trust here, #2,327 by citations
• Reinforcement Learning — 257 citations into it; #733 of 2,242 by trust here, #329 by citations
• Computer Vision and Pattern Recognition — 103 citations into it
• Management Science and Operations Research — 40 citations into it
• Optimization & Training — 39 citations into it
• Computational Theory and Mathematics — 37 citations into it
• Control and Systems Engineering — 36 citations into it
• Evaluation & Benchmarks — 35 citations into it; #607 of 3,147 by trust here, #1,103 by citations

Whom Riashat Islam trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Sergey Levine
5. Yoshua Bengio
6. Richard S. Sutton
7. Alex Krizhevsky
8. Ilya Sutskever
9. John Schulman
10. Ronald J. Williams

Who trusts Riashat Islam overall

1. Horia Mania
2. Che Wang
3. B. Recht
4. Shauharda Khadka
5. Sergey Levine
6. Aurelia Guy
7. Scott Fujimoto
8. Ali Ayub
9. Hiroki Furuta
10. Ilya Kostrikov

Papers from Riashat Islam's perspective

Ordered by how strongly the researchers Riashat Islam 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Reinforcement Learning that Matters (2017) — 2,403 citations
• Deep Bayesian Active Learning with Image Data (2017) — 2,034 citations
• Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control (2017) — 282 citations
• InfoBot: Transfer and Exploration via the Information Bottleneck (2019) — 189 citations
• An Introduction to Deep Reinforcement Learning (2018) — 1,498 citations
• Bayesian Hypernetworks (2017) — 157 citations
• RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms (2018) — 29 citations