Saurabh Tiwary
Artificial Intelligence · 2016–2022
A researcher in the Science corpus: 7 papers; trusts 216 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 236 citations into it
- Natural Language Processing — 184 citations into it; #5,473 of 5,688 by trust here, #1,174 by citations
- Computer Vision and Pattern Recognition — 106 citations into it
- Large Language Models — 79 citations into it
- Evaluation & Benchmarks — 54 citations into it; #846 of 3,147 by trust here, #213 by citations
- Model Architectures — 25 citations into it
- Representation & Self-Supervised Learning — 25 citations into it
- General Social Sciences — 6 citations into it
Whom Saurabh Tiwary trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Alex Krizhevsky
- Yann LeCun
- Alex Graves
- Sepp Hochreiter
- Jacob Devlin
Who trusts Saurabh Tiwary overall
- Sebastian Hofstätter
- Luyu Gao
- Jimmy J. Lin
- Zhuyun Dai
- Rodrigo Nogueira
- Omar Khattab
- Bhaskar Mitra
- Rajarshee Mitra
Papers from Saurabh Tiwary's perspective
Ordered by how strongly the researchers Saurabh Tiwary trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- 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
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model (2022) — 859 citations
- COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining (2021) — 227 citations
- Knowledge-Aware Language Model Pretraining (2020) — 93 citations
- Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators (2022) — 17 citations
- Transformer-XH: Multi-Evidence Reasoning with eXtra Hop Attention (2020) — 125 citations
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (2016) — 524 citations
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset (2016) — 3,491 citations