Ajil Jalal

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 321 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it; #5,692 of 21,575 by trust here, #5,287 by citations
• Computational Mechanics — 89 citations into it; #2,446 of 10,001 by trust here, #1,506 by citations
• Artificial Intelligence — 61 citations into it; #18,952 of 35,345 by trust here, #14,181 by citations
• Generative Models — 55 citations into it; #602 of 1,956 by trust here, #620 by citations
• Safety, Robustness & Interpretability — 40 citations into it; #1,243 of 2,251 by trust here, #1,249 by citations
• Radiology, Nuclear Medicine and Imaging — 39 citations into it
• Optimization & Training — 29 citations into it
• Applied Mathematics — 14 citations into it

Whom Ajil Jalal trusts overall

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

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

Who trusts Ajil Jalal overall

1. Hyungjin Chung
2. Giannis Daras
3. Yang Song
4. Aviad Aberdam
5. A. Dimakis
6. David Stutz
7. Alexander Turner
8. Lukas Schott
9. Reinhard Heckel
10. Dinghuai Zhang

Papers from Ajil Jalal's perspective

Ordered by how strongly the researchers Ajil Jalal 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Compressed Sensing using Generative Models (2017) — 934 citations
• The Robust Manifold Defense: Adversarial Training using Generative Models (2017) — 176 citations
• Intermediate Layer Optimization for Inverse Problems using Deep Generative Models (2021) — 105 citations
• Robust Compressed Sensing MRI with Deep Generative Priors (2021) — 470 citations
• Inverting Deep Generative models, One layer at a time (2019) — 58 citations
• Deep Learning Techniques for Inverse Problems in Imaging (2020) — 670 citations