Sarthak Jain
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 8 papers; trusts 280 researchers and is trusted by 106 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 206 citations into it; #2,363 of 35,345 by trust here, #3,423 by citations
- Natural Language Processing — 109 citations into it; #2,278 of 5,688 by trust here, #1,975 by citations
- Computer Vision and Pattern Recognition — 79 citations into it; #3,717 of 21,575 by trust here, #3,909 by citations
- Safety, Robustness & Interpretability — 59 citations into it; #191 of 2,251 by trust here, #383 by citations
- Generative Models — 40 citations into it
- Representation & Self-Supervised Learning — 35 citations into it
- Evaluation & Benchmarks — 26 citations into it; #2,054 of 3,147 by trust here, #1,517 by citations
- Large Language Models — 25 citations into it
Whom Sarthak Jain trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Ronald J. Williams
- Richard Socher
- Alex Krizhevsky
- Jimmy Ba
- Ilya Sutskever
- Yann LeCun
- Kaiming He
Who trusts Sarthak Jain overall
- Sarah Wiegreffe
- Alon Jacovi
- Ana Marasovic
- Yoav Goldberg
- Xiaochuang Han
- Noah A. Smith
- Michael Sejr Schlichtkrull
- Dheeraj Rajagopal
- Yulia Tsvetkov
- Ilias Chalkidis
Papers from Sarthak Jain's perspective
Ordered by how strongly the researchers Sarthak Jain trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Attention is not Explanation (2019) — 1,871 citations
- Structured Disentangled Representations (2018) — 183 citations
- ERASER: A Benchmark to Evaluate Rationalized NLP Models (2019) — 847 citations
- Learning to Faithfully Rationalize by Construction (2020) — 177 citations
- Does BERT Pretrained on Clinical Notes Reveal Sensitive Data? (2021) — 158 citations
- An Empirical Comparison of Instance Attribution Methods for NLP (2021) — 45 citations
- Combining Feature and Instance Attribution to Detect Artifacts (2021) — 48 citations
- Hierarchical Disentangled Representations (2018) — 22 citations