Nitish Gupta

Artificial Intelligence · 2017–2025

A researcher in the Science corpus: 8 papers; trusts 376 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 212 citations into it; #11,787 of 35,345 by trust here, #5,390 by citations
• Natural Language Processing — 180 citations into it; #2,328 of 5,688 by trust here, #1,813 by citations
• Computer Vision and Pattern Recognition — 82 citations into it; #11,277 of 21,575 by trust here, #4,438 by citations
• Evaluation & Benchmarks — 71 citations into it
• Information Systems — 20 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Model Architectures — 15 citations into it
• Large Language Models — 11 citations into it

Whom Nitish Gupta trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ilya Sutskever
6. Richard Socher
7. Daniel Klein
8. Christopher D. Manning
9. Luke S. Zettlemoyer
10. Kaiming He

Who trusts Nitish Gupta overall

1. Tushar Khot
2. Matt Gardner
3. Yasumasa Onoe
4. Feng Nie
5. Jiangming Liu
6. Jorge Armando Mendez Mendez
7. Avirup Sil
8. Yufei Feng
9. Ori Yoran
10. IV RobertL.Logan

Papers from Nitish Gupta's perspective

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

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

Their papers

• Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020) — 462 citations
• Neural Module Networks for Reasoning over Text (2019) — 134 citations
• Entity Linking via Joint Encoding of Types, Descriptions, and Context (2017) — 225 citations
• Evaluating NLP Models via Contrast Sets (2020) — 85 citations
• Obtaining Faithful Interpretations from Compositional Neural Networks (2020) — 43 citations
• Neural Compositional Denotational Semantics for Question Answering (2018) — 23 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Robust Named Entity Recognition with Truecasing Pretraining (2019) — 43 citations