Sanjay Subramanian

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 7 papers; trusts 361 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 234 citations into it
• Natural Language Processing — 160 citations into it; #4,866 of 5,688 by trust here, #2,567 by citations
• Computer Vision and Pattern Recognition — 131 citations into it
• Evaluation & Benchmarks — 82 citations into it
• Multimodal Learning — 59 citations into it
• Information Systems — 22 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Sanjay Subramanian trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Karen Simonyan
10. Shaoqing Ren

Who trusts Sanjay Subramanian overall

1. Andreas Madsen
2. Zhengzhong Liang
3. Yang Trista Cao
4. Oyvind Tafjord
5. D'idac Sur'is
6. Nora Kassner
7. Siva Reddy
8. Steven Bethard

Papers from Sanjay Subramanian's perspective

Ordered by how strongly the researchers Sanjay Subramanian 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. 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020) — 462 citations
• ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension (2022) — 187 citations
• Evaluating NLP Models via Contrast Sets (2020) — 85 citations
• Obtaining Faithful Interpretations from Compositional Neural Networks (2020) — 43 citations
• AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models (2019) — 146 citations
• Modular Visual Question Answering via Code Generation (2023) — 68 citations
• Evaluation of named entity coreference (2019) — 16 citations