Nasim Rahaman

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 17 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 96 citations into it; #33,623 of 35,345 by trust here, #10,566 by citations
• Model Architectures — 33 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Probabilistic & Bayesian Methods — 14 citations into it
• Representation & Self-Supervised Learning — 14 citations into it; #2,308 of 3,145 by trust here, #1,754 by citations
• Causal Inference & Discovery — 13 citations into it
• Statistics and Probability — 8 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Nasim Rahaman trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Alex Krizhevsky
6. Ian J. Goodfellow
7. Andrew R. Barron
8. Yann LeCun
9. Christian Szegedy
10. Ilya Sutskever

Who trusts Nasim Rahaman overall

1. Zhi-Qin John Xu
2. Greg Yang
3. Yoshua Bengio
4. Albert Pumarola
5. Sara Sabour
6. Yuan Cao
7. Wei Jiang
8. Oscar Michel
9. Mohammad Pezeshki
10. Anirudh Goyal

Papers from Nasim Rahaman's perspective

Ordered by how strongly the researchers Nasim Rahaman 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Coordination Among Neural Modules Through a Shared Global Workspace (2021) — 117 citations
• On the Spectral Bias of Neural Networks (2018) — 2,472 citations
• COVI White Paper (2020) — 30 citations
• A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019) — 379 citations