Alok Sharma

Computer Vision and Pattern Recognition · 2014–2019

A researcher in the Science corpus: 7 papers; trusts 7 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 21 citations into it; #9,621 of 21,575 by trust here, #19,766 by citations
• Artificial Intelligence — 12 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Alok Sharma trusts overall

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

1. Geoffrey E. Hinton
2. Hua Yu
3. Tin Kam Ho
4. Yoshua Bengio
5. Bernhard Schölkopf
6. Yann LeCun
7. Alex Krizhevsky
8. Scott A. Armstrong
9. Jie Yang
10. Sam T. Roweis

Who trusts Alok Sharma overall

1. Alaa Tharwat
2. Yi-Wei Chen
3. Qinxue Meng
4. Qingquan Song
5. Chaopeng Shen
6. Daniel R. Catchpoole
7. Xia Hu
8. Jack Hanson
9. D. Skillicom

Papers from Alok Sharma's perspective

Ordered by how strongly the researchers Alok Sharma 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014) — 152 citations
• Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning (2015) — 346 citations
• SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks. (2017) — 149 citations
• Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016) — 99 citations
• MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles. (2018) — 50 citations
• DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture (2019) — 393 citations
• Linear discriminant analysis for the small sample size problem: an overview (2014) — 259 citations