Tameem Adel

Artificial Intelligence · with Weller · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 347 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #21,618 of 35,345 by trust here, #16,471 by citations
• Computer Vision and Pattern Recognition — 82 citations into it
• Representation & Self-Supervised Learning — 44 citations into it
• Meta & Continual Learning — 31 citations into it; #685 of 1,041 by trust here, #711 by citations
• Generative Models — 24 citations into it; #1,365 of 1,956 by trust here, #1,531 by citations
• Probabilistic & Bayesian Methods — 20 citations into it
• Optimization & Training — 19 citations into it
• Safety, Robustness & Interpretability — 17 citations into it; #1,487 of 2,251 by trust here, #1,569 by citations

Whom Tameem Adel trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yann LeCun
6. Danilo Jimenez Rezende
7. J. Urgen Schmidhuber
8. L. Eon Bottou
9. Max Welling
10. Ian J. Goodfellow

Who trusts Tameem Adel overall

1. Han Zhao
2. Zhi Chen
3. Adrian Weller
4. Francesco Locatello
5. Noel Loo
6. Pranshu Malviya
7. Shipeng Wang
8. Maciej Wolczyk
9. Yonghao Xu
10. Richard E. Turner

Papers from Tameem Adel's perspective

Ordered by how strongly the researchers Tameem Adel 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Learning with Adaptive Weights (CLAW) (2019) — 79 citations
• Getting a CLUE: A Method for Explaining Uncertainty Estimates (2020) — 135 citations
• Discovering Interpretable Representations for Both Deep Generative and Discriminative Models (2018) — 96 citations
• Interpretable Continual Learning (2018)
• Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption (2017) — 28 citations