Divyansh Kaushik

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 6 papers; trusts 256 researchers and is trusted by 74 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 226 citations into it; #3,325 of 35,345 by trust here, #3,878 by citations
• Natural Language Processing — 175 citations into it; #895 of 5,688 by trust here, #943 by citations
• Evaluation & Benchmarks — 87 citations into it; #1,142 of 3,147 by trust here, #1,004 by citations
• Computer Vision and Pattern Recognition — 72 citations into it; #6,653 of 21,575 by trust here, #7,584 by citations
• Safety, Robustness & Interpretability — 51 citations into it
• Representation & Self-Supervised Learning — 26 citations into it; #2,342 of 3,145 by trust here, #2,910 by citations
• Large Language Models — 12 citations into it
• Information Retrieval & Search — 11 citations into it

Whom Divyansh Kaushik trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Graves
4. Yoshua Bengio
5. Alex Krizhevsky
6. Ilya Sutskever
7. J. Urgen Schmidhuber
8. Jimmy Ba
9. Yann LeCun
10. Jacob Devlin

Who trusts Divyansh Kaushik overall

1. Sarah Wiegreffe
2. Patrick Lewis
3. Douwe Kiela
4. Matt Gardner
5. Ana Marasovic
6. Eric Wallace
7. Alon Talmor
8. Amir Feder
9. Abhilasha Ravichander
10. Sebastian Riedel

Papers from Divyansh Kaushik's perspective

Ordered by how strongly the researchers Divyansh Kaushik 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
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. 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

• Learning the Difference that Makes a Difference with Counterfactually-Augmented Data (2019) — 662 citations
• Dynabench: Rethinking Benchmarking in NLP (2021) — 580 citations
• How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (2018) — 239 citations
• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study (2021) — 38 citations
• Explaining The Efficacy of Counterfactually-Augmented Data (2020) — 91 citations
• Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment (2019) — 133 citations