Saibal Mukhopadhyay

Artificial Intelligence · 2003–2017

A researcher in the Science corpus: 5 papers; trusts 31 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it; #25,800 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #21,170 of 21,575 by trust here, #18,782 by citations
• Electrical and Electronic Engineering — 14 citations into it; #11,663 of 16,539 by trust here, #6,070 by citations
• Model Architectures — 11 citations into it
• Safety, Robustness & Interpretability — 10 citations into it
• Hardware and Architecture — 9 citations into it; #1,353 of 1,551 by trust here, #1,427 by citations
• Optimization & Training — 5 citations into it; #2,571 of 2,601 by trust here, #2,313 by citations

Whom Saibal Mukhopadhyay trusts overall

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

1. Alex Krizhevsky
2. Ian J. Goodfellow
3. Alexey Kurakin
4. Kaiming He
5. Shekhar Y. Borkar
6. Christian Szegedy
7. Geoffrey E. Hinton
8. Yann LeCun
9. Yoshua Bengio
10. Sergey Ioffe

Who trusts Saibal Mukhopadhyay overall

1. Smruti Ranjan Sarangi
2. Radu Teodorescu
3. Naveed Akhtar

Papers from Saibal Mukhopadhyay's perspective

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

Their papers

• Cascade Adversarial Machine Learning Regularized with a Unified Embedding (2017) — 107 citations
• On-chip training of recurrent neural networks with limited numerical precision (2017) — 42 citations
• Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory (2016) — 213 citations
• Leakage current mechanisms and leakage reduction techniques in deep-submicrometer CMOS circuits (2003) — 2,523 citations
• Modeling of failure probability and statistical design of SRAM array for yield enhancement in nanoscaled CMOS (2005) — 512 citations