Rana Ali Amjad

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 141 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it; #16,094 of 35,345 by trust here, #10,798 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #10,286 of 21,575 by trust here, #8,296 by citations
• Efficiency & Compression — 40 citations into it; #605 of 1,264 by trust here, #716 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 13 citations into it
• Statistical and Nonlinear Physics — 8 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Safety, Robustness & Interpretability — 5 citations into it

Whom Rana Ali Amjad trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Naftali Tishby
4. Kaiming He
5. Alexander A. Alemi
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Yann LeCun
9. Max Welling
10. X. Zhang

Who trusts Rana Ali Amjad overall

1. Markus Nagel
2. Yelysei Bondarenko
3. Elias Frantar
4. Itay Hubara
5. Adar Elad
6. Jerry Chee
7. Marios Fournarakis
8. Tijmen Blankevoort
9. Ji Lin
10. Dan Alistarh

Papers from Rana Ali Amjad's perspective

Ordered by how strongly the researchers Rana Ali Amjad 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. ImageNet Large Scale Visual Recognition Challenge
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

• Up or Down? Adaptive Rounding for Post-Training Quantization (2020) — 883 citations
• A White Paper on Neural Network Quantization (2021) — 894 citations
• Bayesian Bits: Unifying Quantization and Pruning (2020) — 143 citations
• Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle (2018) — 220 citations