Nakul Gopalan
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 5 papers; trusts 113 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 122 citations into it; #16,972 of 35,345 by trust here, #10,207 by citations
- Natural Language Processing — 107 citations into it; #3,003 of 5,688 by trust here, #2,364 by citations
- Computer Vision and Pattern Recognition — 77 citations into it; #12,836 of 21,575 by trust here, #8,502 by citations
- Reinforcement Learning — 57 citations into it; #1,982 of 2,242 by trust here, #1,204 by citations
- Multimodal Learning — 19 citations into it
- Robotics & Control — 10 citations into it
- LLM Agents & Tool Use — 8 citations into it
- Optimization & Training — 6 citations into it
Whom Nakul Gopalan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Luke S. Zettlemoyer
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Yoshua Bengio
- Yoav Artzi
- Tomas Mikolov
- Andrew Y. Ng
- Richard S. Sutton
Who trusts Nakul Gopalan overall
- Siddharth Karamcheti
- Sahisnu Mazumder
- Prasoon Goyal
- Bing Liu
- Cynthia Matuszek
- Dzmitry Bahdanau
- Valts Blukis
Papers from Nakul Gopalan's perspective
Ordered by how strongly the researchers Nakul Gopalan trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Robots That Use Language (2020) — 312 citations
- Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities (2017) — 64 citations
- LanCon-Learn: Learning with Language to Enable Generalization in Multi-Task Manipulation (2021) — 40 citations
- Learning to Parse Natural Language to Grounded Reward Functions with Weak Supervision (2018) — 61 citations
- A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions (2017) — 17 citations