Varun Kumar

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 6 papers; trusts 66 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 150 citations into it; #12,789 of 35,345 by trust here, #6,103 by citations
• Natural Language Processing — 104 citations into it; #2,426 of 5,688 by trust here, #2,605 by citations
• Large Language Models — 36 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 8 citations into it
• General Social Sciences — 7 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Varun Kumar trusts overall

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

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

Who trusts Varun Kumar overall

1. Timo Schick
2. Steven Y. Feng
3. Omid Kashefi
4. Bohan Li
5. Azal Ahmad Khan
6. Nathan Ng
7. Junnan Li
8. Silei Xu
9. Jiaao Chen
10. Michael A. Hedderich

Papers from Varun Kumar's perspective

Ordered by how strongly the researchers Varun Kumar 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation (2021) — 594 citations
• Data Augmentation using Pre-trained Transformer Models (2020) — 411 citations
• Multi-lingual Evaluation of Code Generation Models (2022) — 247 citations
• On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations (2022) — 112 citations
• A Closer Look At Feature Space Data Augmentation For Few-Shot Intent Classification (2019) — 91 citations
• ReCode: Robustness Evaluation of Code Generation Models (2022) — 161 citations