Mohammad Haris Baig

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 394 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #6,920 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 54 citations into it; #9,980 of 35,345 by trust here, #10,942 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 13 citations into it
• Optimization & Training — 9 citations into it
• Generative Models — 7 citations into it

Whom Mohammad Haris Baig trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Karen Simonyan
9. Jia Deng
10. Ross B. Girshick

Who trusts Mohammad Haris Baig overall

1. Jian Liang
2. Lin Wang
3. Karim Ahmed
4. Mohammad Rostami
5. Dapeng Hu
6. Xiaobin Chang
7. Ravi Teja Mullapudi
8. Alvin Wan
9. Rahaf Aljundi
10. Xin Eric Wang

Papers from Mohammad Haris Baig's perspective

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

• Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation (2019) — 606 citations
• Network of Experts for Large-Scale Image Categorization (2016) — 142 citations