Tuhin Chakrabarty

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 6 papers; trusts 242 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #25,572 of 35,345 by trust here, #14,181 by citations
• Natural Language Processing — 104 citations into it
• Computer Vision and Pattern Recognition — 62 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Large Language Models — 35 citations into it; #579 of 799 by trust here, #689 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Prompting & Reasoning — 13 citations into it
• Experimental and Cognitive Psychology — 8 citations into it

Whom Tuhin Chakrabarty trusts overall

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

1. Geoffrey E. Hinton
2. Angela Fan
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Michael Lewis
8. Yann LeCun
9. Kaiming He
10. Alec Radford

Who trusts Tuhin Chakrabarty overall

1. Zixuan Ke
2. Marzena Karpinska
3. Jena D. Hwang
4. Piotr Wojciech Mirowski
5. Denis Emelin

Papers from Tuhin Chakrabarty's perspective

Ordered by how strongly the researchers Tuhin Chakrabarty trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
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

• Continual-T0: Progressively Instructing 50+ Tasks to Language Models Without Forgetting (2022) — 22 citations
• Content Planning for Neural Story Generation with Aristotelian Rescoring (2020) — 141 citations
• R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge (2020) — 70 citations
• Fine-tuned Language Models are Continual Learners (2022) — 185 citations
• Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022) — 108 citations
• MERMAID: Metaphor Generation with Symbolism and Discriminative Decoding (2021) — 86 citations