Ishan Durugkar

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it; #6,130 of 21,575 by trust here, #8,502 by citations
• Generative Models — 17 citations into it; #588 of 1,956 by trust here, #961 by citations
• Management Science and Operations Research — 14 citations into it
• Reinforcement Learning — 14 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 7 citations into it

Whom Ishan Durugkar trusts overall

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

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

Who trusts Ishan Durugkar overall

1. Animesh Karnewar
2. Eli Schwartz
3. Garrett Wilson
4. Isabela Albuquerque
5. Dan Zhang
6. Tero Karras
7. Oliver Wang
8. Zizhao Zhang
9. Behnam Neyshabur
10. Gonçalo Mordido

Papers from Ishan Durugkar's perspective

Ordered by how strongly the researchers Ishan Durugkar 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Generative Multi-Adversarial Networks (2016) — 368 citations
• Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017) — 615 citations
• TD Learning with Constrained Gradients (2018) — 17 citations
• Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing (2017) — 56 citations