Subhabrata Mukherjee

Artificial Intelligence · 2016–2024

A researcher in the Science corpus: 6 papers; trusts 123 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #14,615 of 35,345 by trust here, #8,893 by citations
• Natural Language Processing — 48 citations into it; #2,896 of 5,688 by trust here, #3,107 by citations
• Large Language Models — 31 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it; #10,996 of 21,575 by trust here, #17,258 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Information Systems — 23 citations into it; #8,532 of 8,771 by trust here, #3,402 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Sociology and Political Science — 19 citations into it; #22,134 of 22,756 by trust here, #8,985 by citations

Whom Subhabrata Mukherjee trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Jimmy Ba
4. Richard Socher
5. Alec Radford
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ilya Sutskever
9. Yann LeCun
10. Alex Graves

Who trusts Subhabrata Mukherjee overall

1. Wenhui Wang
2. Manish Gupta
3. Hyung Won Chung
4. Puneet Agrawal
5. Kai Han
6. Ingo Ziegler
7. Hangbo Bao
8. Furu Wei
9. Peiyi Wang
10. Liang Wang

Papers from Subhabrata Mukherjee's perspective

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

Their papers

• Orca: Progressive Learning from Complex Explanation Traces of GPT-4 (2023) — 421 citations
• Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer (2020) — 97 citations
• XtremeDistil: Multi-stage Distillation for Massive Multilingual Models (2020) — 65 citations
• Credibility Assessment of Textual Claims on the Web (2016) — 201 citations
• Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing (2024) — 356 citations
• DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018) — 367 citations