Anil Anthony Bharath

Artificial Intelligence · 2002–2017

A researcher in the Science corpus: 7 papers; trusts 336 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 159 citations into it; #18,204 of 35,345 by trust here, #7,945 by citations
• Reinforcement Learning — 150 citations into it; #1,672 of 2,242 by trust here, #762 by citations
• Computer Vision and Pattern Recognition — 100 citations into it; #3,627 of 21,575 by trust here, #3,035 by citations
• Generative Models — 60 citations into it; #293 of 1,956 by trust here, #540 by citations
• Optimization & Training — 18 citations into it
• Statistical and Nonlinear Physics — 17 citations into it
• Control and Systems Engineering — 15 citations into it
• Model Architectures — 14 citations into it

Whom Anil Anthony Bharath trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Richard S. Sutton
7. Yann LeCun
8. Kaiming He
9. J. Urgen Schmidhuber
10. Alex Graves

Who trusts Anil Anthony Bharath overall

1. Yuval Alaluf
2. Yotam Nitzan
3. Rameen Abdal
4. Yujun Shen
5. Omer Tov
6. Jiapeng Zhu
7. Michael Albright
8. Daiqing Li
9. Axel Sauer
10. Kyoungkook Kang

Papers from Anil Anthony Bharath's perspective

Ordered by how strongly the researchers Anil Anthony Bharath 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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Inverting the Generator of a Generative Adversarial Network (2016) — 369 citations
• A Brief Survey of Deep Reinforcement Learning (2017) — 3,581 citations
• Generative Adversarial Networks: An Overview (2017) — 3,805 citations
• A data augmentation methodology for training machine/deep learning gait recognition algorithms (2016) — 43 citations
• Classifying Options for Deep Reinforcement Learning (2016) — 22 citations
• Segmentation of blood vessels from red-free and fluorescein retinal images (2007) — 471 citations
• Retinal vascular tree morphology: a semi-automatic quantification (2002) — 295 citations