Latifur R. Khan

Artificial Intelligence · 2007–2021

A researcher in the Science corpus: 5 papers; trusts 71 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #16,650 of 35,345 by trust here, #17,776 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Signal Processing — 15 citations into it; #5,143 of 7,572 by trust here, #6,083 by citations
• Computer Networks and Communications — 12 citations into it; #3,041 of 9,886 by trust here, #4,277 by citations
• Information Systems — 7 citations into it; #5,576 of 8,771 by trust here, #7,107 by citations
• Evaluation & Benchmarks — 5 citations into it

Whom Latifur R. Khan trusts overall

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

1. Chih-Jen Lin
2. Gert Cauwenberghs
3. S. Ray
4. Geoffrey E. Hinton
5. Tian Zhang
6. Hwanjo Yu
7. Alex Krizhevsky
8. Vladimir Naumovich Vapni
9. Bernhard Schölkopf
10. Diederik P. Kingma

Who trusts Latifur R. Khan overall

1. Sumaiya Thaseen Ikram
2. Monowar Hussain Bhuyan
3. Wei‐Chao Lin
4. Preeti Mishra
5. Kamran Shaukat
6. Giuseppe Ateniese
7. V. Varadharajan
8. Cherukuri Aswani Kumar
9. Nikola Milosevic
10. Dhruba Kumar Bhattacharyya

Papers from Latifur R. Khan's perspective

Ordered by how strongly the researchers Latifur R. Khan 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• CIFDM: Continual and Interactive Feature Distillation for Multi-Label Stream Learning (2021) — 19 citations
• CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining (2021) — 21 citations
• A Machine Learning Approach to Android Malware Detection (2012) — 371 citations
• Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints (2011) — 415 citations
• A new intrusion detection system using support vector machines and hierarchical clustering (2007) — 462 citations