Swarnadeep Saha
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 5 papers; trusts 278 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 189 citations into it; #23,706 of 35,345 by trust here, #20,377 by citations
- Natural Language Processing — 138 citations into it; #3,239 of 5,688 by trust here, #3,687 by citations
- Computer Vision and Pattern Recognition — 74 citations into it
- Evaluation & Benchmarks — 52 citations into it
- Prompting & Reasoning — 36 citations into it
- Large Language Models — 32 citations into it
- Safety, Robustness & Interpretability — 19 citations into it
- Model Architectures — 6 citations into it
Whom Swarnadeep Saha trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Alex Graves
- Richard Socher
- Jason Aaron Edward Weston
Who trusts Swarnadeep Saha overall
- Gregor Betz
- Amrith Krishna
- Prateek Yadav
- Antonia Creswell
- Kaj Bostrom
- Aman Madaan
- Mohit Bansal
- Kyle Richardson
- Sebastian Riedel
- Murray Shanahan
Papers from Swarnadeep Saha's perspective
Ordered by how strongly the researchers Swarnadeep Saha trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- 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
- PRover: Proof Generation for Interpretable Reasoning over Rules (2020) — 84 citations
- ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs (2023) — 336 citations
- Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations (2022) — 18 citations
- multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning (2021) — 29 citations
- ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning (2021) — 83 citations