Shiva Krishna Reddy Malay

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 246 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 47 citations into it; #9,801 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 35 citations into it; #15,390 of 35,345 by trust here, #14,718 by citations
• Radiology, Nuclear Medicine and Imaging — 16 citations into it; #2,471 of 3,265 by trust here, #1,861 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Generative Models — 14 citations into it
• Meta & Continual Learning — 8 citations into it

Whom Shiva Krishna Reddy Malay trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Karen Simonyan
8. Kaiming He
9. Jimmy Ba
10. Jia Deng

Who trusts Shiva Krishna Reddy Malay overall

1. Ayan Kumar Bhunia
2. Anjan Dutta
3. Aneeshan Sain
4. Raghavendra Chalapathy
5. Jun Shu
6. Edgar Schönfeld
7. Qing Liu
8. Chen Xing
9. Gundeep Arora
10. Zongyan Han

Papers from Shiva Krishna Reddy Malay's perspective

Ordered by how strongly the researchers Shiva Krishna Reddy Malay 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Generative Model for Zero Shot Learning Using Conditional Variational Autoencoders (2017) — 328 citations
• A Zero-Shot Framework for Sketch-based Image Retrieval (2018) — 217 citations