Shashank Srivastava

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 123 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #9,607 of 35,345 by trust here, #8,210 by citations
• Natural Language Processing — 69 citations into it; #2,189 of 5,688 by trust here, #2,846 by citations
• Computer Vision and Pattern Recognition — 38 citations into it; #14,072 of 21,575 by trust here, #9,079 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Prompting & Reasoning — 8 citations into it; #329 of 359 by trust here, #237 by citations
• Information Systems — 5 citations into it

Whom Shashank Srivastava trusts overall

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

1. Percy Liang
2. Jayant Krishnamurthy
3. Geoffrey E. Hinton
4. Luke S. Zettlemoyer
5. Gideon S. Mann
6. Yoshua Bengio
7. Michael Collins
8. Michael I. Jordan
9. Diederik P. Kingma
10. Kuzman Ganchev

Who trusts Shashank Srivastava overall

1. Peter Hase
2. Mohit Bansal
3. Qinyuan Ye
4. Jacob Andreas
5. Igor Labutov
6. Ziqi Wang
7. Theodore R. Sumers
8. Wangchunshu Zhou
9. Ahmed Elgohary
10. Swarnadeep Saha

Papers from Shashank Srivastava's perspective

Ordered by how strongly the researchers Shashank Srivastava 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Joint Concept Learning and Semantic Parsing from Natural Language Explanations (2017) — 96 citations
• Improving and Simplifying Pattern Exploiting Training (2021) — 157 citations
• Zero-shot Learning of Classifiers from Natural Language Quantification (2018) — 59 citations
• PRover: Proof Generation for Interpretable Reasoning over Rules (2020) — 84 citations
• Learning Classifiers from Declarative Language (2018)