Satwik Kottur
Computer Vision and Pattern Recognition · 2016–2021
A researcher in the Science corpus: 7 papers; trusts 482 researchers and is trusted by 233 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 294 citations into it; #2,635 of 21,575 by trust here, #1,698 by citations
- Artificial Intelligence — 197 citations into it; #3,544 of 35,345 by trust here, #1,753 by citations
- Natural Language Processing — 102 citations into it; #2,927 of 5,688 by trust here, #1,150 by citations
- Multimodal Learning — 94 citations into it; #272 of 1,211 by trust here, #205 by citations
- Model Architectures — 31 citations into it; #884 of 2,278 by trust here, #461 by citations
- Reinforcement Learning — 22 citations into it; #1,650 of 2,242 by trust here, #738 by citations
- Optimization & Training — 13 citations into it
- Management Science and Operations Research — 6 citations into it
Whom Satwik Kottur trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Kaiming He
- Yoshua Bengio
- Geoffrey E. Hinton
- Karen Simonyan
- Alex Krizhevsky
- Jimmy Ba
- Ilya Sutskever
- Ronald J. Williams
- Yann LeCun
Who trusts Satwik Kottur overall
- Gi-Cheon Kang
- Dhruv Batra
- Devi Parikh
- Zhe Gan
- Paul Hongsuck Seo
- Qi Wu
- Hanwang Zhang
- Sang-Woo Lee
- Abhishek Das
- Van-Quang Nguyen
Papers from Satwik Kottur's perspective
Ordered by how strongly the researchers Satwik Kottur trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Visual Dialog (2016) — 1,102 citations
- Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning (2017) — 433 citations
- Deep Sets (2017) — 3,061 citations
- Ego4D: Around the World in 3,000 Hours of Egocentric Video (2021) — 1,925 citations
- Visual Coreference Resolution in Visual Dialog using Neural Module Networks (2018) — 174 citations
- Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog (2017) — 239 citations
- CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog (2019) — 94 citations