Jivko Sinapov

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 8 papers; trusts 81 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #23,380 of 35,345 by trust here, #15,542 by citations
• Reinforcement Learning — 60 citations into it; #2,142 of 2,242 by trust here, #1,689 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #20,763 of 21,575 by trust here, #15,529 by citations
• Natural Language Processing — 31 citations into it; #5,069 of 5,688 by trust here, #3,478 by citations
• Meta & Continual Learning — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it; #2,285 of 3,145 by trust here, #2,863 by citations
• Computational Theory and Mathematics — 11 citations into it
• Control and Systems Engineering — 10 citations into it; #5,882 of 8,257 by trust here, #5,784 by citations

Whom Jivko Sinapov trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Matthew E. Taylor
4. Karen Simonyan
5. Peter Stone
6. Rob Fergus
7. Geoffrey E. Hinton
8. Andrew Zisserman
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Jivko Sinapov overall

1. Jesse Thomason
2. Sanmit Narvekar
3. Peter Stone
4. Jeannette Bohg
5. Yonatan Bisk
6. Raymond J. Mooney
7. Weixun Wang
8. Aishwarya Padmakumar
9. Pascal Klink
10. Cynthia Matuszek

Papers from Jivko Sinapov's perspective

Ordered by how strongly the researchers Jivko Sinapov 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog (2020) — 60 citations
• Guiding Exploratory Behaviors for Multi-Modal Grounding of Linguistic Descriptions (2018) — 35 citations
• Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning (2017) — 123 citations
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020) — 748 citations
• Opportunistic Active Learning for Grounding Natural Language Descriptions (2017) — 49 citations
• Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" (2016) — 104 citations
• Source Task Creation for Curriculum Learning (2016) — 130 citations
• Learning relational object categories using behavioral exploration and multimodal perception (2014) — 63 citations