Shyam Upadhyay

Artificial Intelligence · 2015–2022

A researcher in the Science corpus: 11 papers; trusts 204 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #5,732 of 35,345 by trust here, #4,018 by citations
• Natural Language Processing — 147 citations into it; #1,204 of 5,688 by trust here, #981 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #7,630 of 21,575 by trust here, #4,726 by citations
• Evaluation & Benchmarks — 28 citations into it; #2,194 of 3,147 by trust here, #601 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Information Systems — 9 citations into it; #2,893 of 8,771 by trust here, #4,682 by citations
• Model Architectures — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Shyam Upadhyay trusts overall

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

1. Michael Collins
2. Christopher D. Manning
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Luke S. Zettlemoyer
7. Tomas Mikolov
8. Diederik P. Kingma
9. Richard Socher
10. Alex Krizhevsky

Who trusts Shyam Upadhyay overall

1. José Camacho-Collados
2. Danqing Huang
3. Swaroop Mishra
4. Benjamin Robaidek
5. Yixuan Cao
6. Qianying Liu
7. Daniel Khashabi
8. Chao-Chun Liang
9. Mikel Artetxe
10. Bugeun Kim

Papers from Shyam Upadhyay's perspective

Ordered by how strongly the researchers Shyam Upadhyay 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences (2018) — 593 citations
• Cross-lingual Models of Word Embeddings: An Empirical Comparison (2016) — 190 citations
• Attention Interpretability Across NLP Tasks (2019) — 191 citations
• Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems (2016) — 64 citations
• Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems (2016) — 52 citations
• Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering (2021) — 37 citations
• Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context (2017) — 27 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• CogCompNLP: Your Swiss Army Knife for NLP (2018) — 29 citations
• DRAW: A Challenging and Diverse Algebra Word Problem Set (2015) — 20 citations
• The University of Illinois submission to the WMT 2015 Shared Translation Task (2015)