Soumik Sarkar

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 65 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it; #6,052 of 21,575 by trust here, #8,952 by citations
• Artificial Intelligence — 48 citations into it; #11,581 of 35,345 by trust here, #17,412 by citations
• Optimization & Training — 25 citations into it
• Computer Networks and Communications — 13 citations into it; #2,442 of 9,886 by trust here, #3,426 by citations
• ML Systems & Hardware — 8 citations into it
• Computational Mechanics — 7 citations into it
• Signal Processing — 6 citations into it

Whom Soumik Sarkar trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. L. Eon Bottou
7. Xavier Glorot
8. Karen Simonyan
9. Diederik P. Kingma
10. Kaiming He

Who trusts Soumik Sarkar overall

1. Wenhan Yang
2. Chongyi Li
3. Jianyu Wang
4. Hao Yu
5. Wenjing Wang
6. Jiaying Liu
7. Chunle Guo
8. Lili Li
9. Chen Wei
10. Muhammad Hammad Saleem

Papers from Soumik Sarkar's perspective

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

Their papers

• LLNet: A deep autoencoder approach to natural low-light image enhancement (2015) — 1,822 citations
• Collaborative Deep Learning in Fixed Topology Networks (2017) — 201 citations
• Plant disease identification using explainable 3D deep learning on hyperspectral images (2019) — 336 citations
• Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives. (2018) — 510 citations
• An explainable deep machine vision framework for plant stress phenotyping (2018) — 472 citations
• Machine Learning for High-Throughput Stress Phenotyping in Plants. (2016) — 928 citations
• Path planning in GPS-denied environments via collective intelligence of distributed sensor networks (2016) — 15 citations