Kumar Shridhar

Artificial Intelligence · with Wang · 2022–2023

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #28,175 of 35,345 by trust here, #30,347 by citations
• Natural Language Processing — 29 citations into it
• Large Language Models — 26 citations into it; #564 of 799 by trust here, #302 by citations
• Prompting & Reasoning — 19 citations into it; #236 of 359 by trust here, #324 by citations
• Computer Vision and Pattern Recognition — 17 citations into it
• Information Systems — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Kumar Shridhar trusts overall

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

1. Geoffrey E. Hinton
2. Matt Post
3. Yoshua Bengio
4. Kyunghyun Cho
5. Ilya Sutskever
6. Diederik P. Kingma
7. Oriol Vinyals
8. Alec Radford
9. Alex Krizhevsky
10. Ashish Vaswani

Who trusts Kumar Shridhar overall

1. Zirui Zhao
2. Jiaxin Huang
3. Grégoire Mialon
4. Wee Sun Lee
5. Jie Huang
6. Shixiang Shane Gu

Papers from Kumar Shridhar's perspective

Ordered by how strongly the researchers Kumar Shridhar 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. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
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

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
• Distilling Multi-Step Reasoning Capabilities of Large Language Models into Smaller Models via Semantic Decompositions (2022) — 49 citations
• Distilling Reasoning Capabilities into Smaller Language Models (2022) — 270 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations