Raul Puri

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 8 papers; trusts 378 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 255 citations into it; #5,313 of 35,345 by trust here, #923 by citations
• Natural Language Processing — 176 citations into it; #2,149 of 5,688 by trust here, #1,769 by citations
• Computer Vision and Pattern Recognition — 127 citations into it; #7,236 of 21,575 by trust here, #11,721 by citations
• Representation & Self-Supervised Learning — 66 citations into it
• Large Language Models — 61 citations into it; #645 of 799 by trust here, #631 by citations
• Evaluation & Benchmarks — 42 citations into it
• Information Systems — 34 citations into it
• Optimization & Training — 30 citations into it; #1,227 of 2,601 by trust here, #325 by citations

Whom Raul Puri trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Alec Radford
8. Richard Socher
9. Yann LeCun
10. Karen Simonyan

Who trusts Raul Puri overall

1. Timo Schick
2. Hinrich Schütze
3. Teven Le Scao
4. Tal Ben-Nun
5. Robin Jia
6. Alexander M. Rush
7. Samyam Rajbhandari
8. Joel Hestness
9. Tri Dao
10. Jiacheng Liu

Papers from Raul Puri's perspective

Ordered by how strongly the researchers Raul Puri 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Evaluating Large Language Models Trained on Code (2021) — 10,663 citations
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism (2019) — 2,986 citations
• Large Scale Language Modeling: Converging on 40GB of Text in Four Hours (2018) — 31 citations
• Zero-shot Text Classification With Generative Language Models (2019) — 121 citations
• Controllable Story Generation with External Knowledge Using Large-Scale Language Models (2020) — 140 citations
• Text and Code Embeddings by Contrastive Pre-Training (2022) — 613 citations
• Bio-Megatron: Larger Biomedical Domain Language Model (2020) — 177 citations
• Training Question Answering Models from Synthetic Data (2020) — 179 citations