Saurabh Prasad

Computer Vision and Pattern Recognition · 2008–2019

A researcher in the Science corpus: 6 papers; trusts 68 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #10,093 of 21,575 by trust here, #12,689 by citations
• Media Technology — 47 citations into it; #640 of 3,621 by trust here, #559 by citations
• Artificial Intelligence — 33 citations into it; #15,091 of 35,345 by trust here, #16,771 by citations
• Atmospheric Science — 26 citations into it
• Analytical Chemistry — 12 citations into it; #383 of 1,254 by trust here, #478 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Model Architectures — 11 citations into it
• Ecology — 9 citations into it

Whom Saurabh Prasad trusts overall

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

1. Radford M. Neal
2. Balázs Kégl
3. Geoffrey E. Hinton
4. Paul J. Werbos
5. Lorenzo Bruzzone
6. Yoshua Bengio
7. Michael I. Jordan
8. Alex Graves
9. Alex Krizhevsky
10. Shigeo Abe

Who trusts Saurabh Prasad overall

1. Leyuan Fang
2. Shutao Li
3. Guangrui Zhao
4. Wei Li
5. Pedram Ghamisi
6. Sen Jia
7. Nicolas Audebert
8. Yuhui Zheng
9. Renlong Hang
10. Jon Atli Benediktsson

Papers from Saurabh Prasad's perspective

Ordered by how strongly the researchers Saurabh Prasad 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. Long Short-Term Memory
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

• Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification (2018) — 388 citations
• Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest (2019) — 308 citations
• Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest (2014) — 619 citations
• Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014) — 292 citations
• Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis (2012) — 457 citations
• Limitations of Principal Components Analysis for Hyperspectral Target Recognition (2008) — 327 citations