Sam Shleifer

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 14 papers; trusts 413 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 491 citations into it; #8,003 of 35,345 by trust here, #1,914 by citations
• Natural Language Processing — 319 citations into it; #3,123 of 5,688 by trust here, #2,767 by citations
• Computer Vision and Pattern Recognition — 208 citations into it; #8,606 of 21,575 by trust here, #9,187 by citations
• Large Language Models — 164 citations into it; #506 of 799 by trust here, #87 by citations
• Evaluation & Benchmarks — 114 citations into it
• Efficiency & Compression — 93 citations into it; #707 of 1,264 by trust here, #613 by citations
• Optimization & Training — 55 citations into it; #1,738 of 2,601 by trust here, #1,335 by citations
• Model Architectures — 44 citations into it; #1,794 of 2,278 by trust here, #253 by citations

Whom Sam Shleifer trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Noam Shazeer
5. Alex Krizhevsky
6. Ilya Sutskever
7. Kaiming He
8. Jimmy Ba
9. Yann LeCun
10. Oriol Vinyals

Who trusts Sam Shleifer overall

1. Hongyu Wang
2. Barret Zoph
3. James Lee-Thorp
4. François Lagunas
5. Manoj Kumar
6. Tim Dettmers
7. Weihao Yu
8. Shuming Ma
9. Peng Wang
10. Mikel Artetxe

Papers from Sam Shleifer's perspective

Ordered by how strongly the researchers Sam Shleifer 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Gradient-based learning applied to document recognition
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019) — 4,184 citations
• Efficient Large Scale Language Modeling with Mixtures of Experts (2021) — 248 citations
• PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel (2023) — 795 citations
• 8-bit Optimizers via Block-wise Quantization (2021) — 509 citations
• Few-shot Learning with Multilingual Generative Language Models (2021) — 384 citations
• NormFormer: Improved Transformer Pretraining with Extra Normalization (2021) — 97 citations
• Pre-trained Summarization Distillation (2020) — 125 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2023) — 266 citations
• OPT: Open Pre-trained Transformer Language Models (2022) — 4,844 citations
• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models (2022) — 2,561 citations
• Few-shot Learning with Multilingual Language Models (2021) — 281 citations
• Transformers: State-of-the-Art Natural Language Processing (2020) — 24 citations
• HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019)