Sonal Gupta

Artificial Intelligence · 2014–2022

A researcher in the Science corpus: 16 papers; trusts 381 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 345 citations into it; #9,196 of 35,345 by trust here, #4,137 by citations
• Natural Language Processing — 285 citations into it; #2,937 of 5,688 by trust here, #1,857 by citations
• Computer Vision and Pattern Recognition — 154 citations into it
• Evaluation & Benchmarks — 90 citations into it
• Information Retrieval & Search — 70 citations into it
• Large Language Models — 67 citations into it
• Representation & Self-Supervised Learning — 36 citations into it; #2,725 of 3,145 by trust here, #1,479 by citations
• Information Systems — 26 citations into it

Whom Sonal 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. Percy Liang
5. Ilya Sutskever
6. Alex Krizhevsky
7. Yann LeCun
8. Richard Socher
9. Ellen Riloff
10. Kaiming He

Who trusts Sonal Gupta overall

1. Alexander J. Ratner
2. Marco Antonio Valenzuela-Escarcega
3. Panupong Pasupat
4. Haoran Li
5. Marco Damonte
6. Chengyu Wang
7. Giovanni Campagna
8. Subendhu Rongali
9. Yinhe Zheng
10. Genta Indra Winata

Papers from Sonal Gupta's perspective

Ordered by how strongly the researchers Sonal 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• 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
• Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning (2020) — 936 citations
• Semantic Parsing for Task Oriented Dialog using Hierarchical Representations (2018) — 213 citations
• Domain-matched Pre-training Tasks for Dense Retrieval (2021) — 72 citations
• Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog (2018) — 321 citations
• UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering (2020) — 106 citations
• Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One? (2021) — 87 citations
• CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training (2021) — 16 citations
• PyText: A Seamless Path from NLP research to production (2018) — 17 citations
• Make-A-Video: Text-to-Video Generation without Text-Video Data (2022) — 2,111 citations
• Getting to Production with Few-shot Natural Language Generation Models (2021) — 17 citations
• Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task (2020)
• Distantly supervised information extraction using bootstrapped patterns (2015)
• Improved Pattern Learning for Bootstrapped Entity Extraction (2014) — 142 citations