Shoaib Ahmed Siddiqui

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 96 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it; #30,945 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Safety, Robustness & Interpretability — 34 citations into it
• Signal Processing — 17 citations into it; #6,631 of 7,572 by trust here, #4,560 by citations
• Optimization & Training — 16 citations into it
• Statistics and Probability — 13 citations into it
• Natural Language Processing — 12 citations into it
• Computer Networks and Communications — 10 citations into it; #8,534 of 9,886 by trust here, #5,733 by citations

Whom Shoaib Ahmed Siddiqui 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. Diederik P. Kingma
6. Ilya Sutskever
7. Yann LeCun
8. Ian J. Goodfellow
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Shoaib Ahmed Siddiqui overall

1. Ailin Deng
2. Raghavendra Chalapathy
3. Sebastian Schmidl
4. Jihun Yi
5. Guansong Pang
6. Kukjin Choi
7. Ane Blázquez-García
8. Zekai Chen
9. Astha Garg

Papers from Shoaib Ahmed Siddiqui's perspective

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

Their papers

• Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics (2022) — 36 citations
• DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019) — 618 citations
• Deep One-Class Classification (2018) — 2,692 citations
• TSViz: Demystification of Deep Learning Models for Time-Series Analysis (2018) — 94 citations