Piyush Sharma

Artificial Intelligence · with Soricut · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 816 researchers and is trusted by 1,621 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #2,345 of 35,345 by trust here, #1,215 by citations
• Computer Vision and Pattern Recognition — 162 citations into it; #1,028 of 21,575 by trust here, #604 by citations
• Natural Language Processing — 92 citations into it
• Multimodal Learning — 76 citations into it; #74 of 1,211 by trust here, #168 by citations
• Evaluation & Benchmarks — 44 citations into it; #289 of 3,147 by trust here, #364 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Model Architectures — 25 citations into it
• Large Language Models — 23 citations into it; #147 of 799 by trust here, #147 by citations

Whom Piyush Sharma trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Kaiming He
5. Ilya Sutskever
6. Diederik P. Kingma
7. Ross B. Girshick
8. Yann LeCun
9. Christian Szegedy
10. Jimmy Ba

Who trusts Piyush Sharma overall

1. Zhe Gan
2. Jianfeng Wang
3. Soravit Changpinyo
4. Junnan Li
5. Xiaowei Hu
6. Jiasen Lu
7. Zhengyuan Yang
8. Jing Yu Koh
9. Bo Li
10. Linjie Li

Papers from Piyush Sharma's perspective

Ordered by how strongly the researchers Piyush Sharma 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning (2018) — 3,041 citations
• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (2019) — 7,601 citations
• Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts (2021) — 1,502 citations
• Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering (2019) — 20 citations
• Understanding Guided Image Captioning Performance across Domains (2020) — 32 citations