Mohsin Munir

Artificial Intelligence · 2018–2019

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

Subfields

• Artificial Intelligence — 45 citations into it; #30,946 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Signal Processing — 12 citations into it; #6,632 of 7,572 by trust here, #4,560 by citations
• Computer Networks and Communications — 5 citations into it; #8,535 of 9,886 by trust here, #5,733 by citations
• Statistics and Probability — 5 citations into it

Whom Mohsin Munir trusts overall

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

1. Alex Krizhevsky
2. Rfdo 2XWOLHUV
3. Charu C. Aggarwal
4. Geoffrey E. Hinton
5. Alexander Lavin
6. Subutai Ahmad
7. Ilya Sutskever
8. Barret Zoph
9. Ryan P. Adams
10. Yoshua Bengio

Who trusts Mohsin Munir overall

1. Ailin Deng
2. Sebastian Schmidl
3. Kukjin Choi
4. Ane Blázquez-García
5. Zekai Chen
6. Astha Garg
7. Yi Liu
8. Bryan Hooi
9. Phillip Wenig
10. Jihun Yi

Papers from Mohsin Munir's perspective

Ordered by how strongly the researchers Mohsin Munir 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019) — 618 citations
• TSViz: Demystification of Deep Learning Models for Time-Series Analysis (2018) — 94 citations