Anchit Gupta

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 125 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #26,386 of 35,345 by trust here, #7,413 by citations
• Natural Language Processing — 130 citations into it
• Computer Vision and Pattern Recognition — 86 citations into it
• Evaluation & Benchmarks — 65 citations into it; #3,011 of 3,147 by trust here, #2,166 by citations
• Information Retrieval & Search — 47 citations into it
• Reinforcement Learning — 42 citations into it; #2,196 of 2,242 by trust here, #1,519 by citations
• Large Language Models — 32 citations into it
• Control and Systems Engineering — 31 citations into it

Whom Anchit Gupta trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Alex Graves
9. Jimmy Ba
10. Richard Socher

Who trusts Anchit Gupta overall

1. Armen Aghajanyan
2. Swaroop Mishra
3. Vishakh Padmakumar
4. Yi Tay
5. Ajay Mandlekar
6. Ruiqi Zhong
7. Wayne Xin Zhao

Papers from Anchit Gupta's perspective

Ordered by how strongly the researchers Anchit Gupta trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Muppet: Massive Multi-task Representations with Pre-Finetuning (2021) — 301 citations
• Better Fine-Tuning by Reducing Representational Collapse (2020) — 241 citations
• MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark (2020) — 223 citations
• Domain-matched Pre-training Tasks for Dense Retrieval (2021) — 72 citations
• Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One? (2021) — 87 citations
• ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation (2018) — 387 citations
• SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark (2018) — 167 citations