Athul Paul Jacob

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 206 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it; #6,356 of 21,575 by trust here, #4,658 by citations
• Generative Models — 48 citations into it; #641 of 1,956 by trust here, #515 by citations
• Reinforcement Learning — 34 citations into it
• Natural Language Processing — 28 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Optimization & Training — 15 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Athul Paul Jacob trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ronald J. Williams
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. Kaiming He
8. Yann LeCun
9. Jimmy Ba
10. Ruslan Salakhutdinov

Who trusts Athul Paul Jacob overall

1. Yongjun Hong
2. Martin Heusel
3. Thomas Unterthiner
4. Jie Gui
5. Aditya Grover
6. Abdul Jabbar
7. Hubert Ramsauer
8. Uiwon Hwang
9. Ngoc-Trung Tran
10. William Fedus

Papers from Athul Paul Jacob's perspective

Ordered by how strongly the researchers Athul Paul Jacob 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Mode Regularized Generative Adversarial Networks (2016) — 593 citations
• Boundary-Seeking Generative Adversarial Networks (2017) — 176 citations
• Human-level play in the game of Diplomacy by combining language models with strategic reasoning (2022) — 592 citations
• Multitasking Inhibits Semantic Drift (2021) — 13 citations