Benoit Steiner

Artificial Intelligence · 2016–2023

A researcher in the Science corpus: 7 papers; trusts 147 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #9,558 of 35,345 by trust here, #230 by citations
• Computer Vision and Pattern Recognition — 91 citations into it; #14,789 of 21,575 by trust here, #597 by citations
• Model Architectures — 46 citations into it
• Optimization & Training — 39 citations into it
• Efficiency & Compression — 27 citations into it
• Reinforcement Learning — 12 citations into it
• Scaling & Learning Theory — 5 citations into it

Whom Benoit Steiner trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Ilya Sutskever
6. Diederik P. Kingma
7. Christian Szegedy
8. Yoshua Bengio
9. Yann LeCun
10. Mingxing Tan

Who trusts Benoit Steiner overall

1. Quoc V. Le
2. Matei A. Zaharia
3. Vladlen Koltun
4. Ali Ayub
5. Eric P. Xing
6. Mohit Bansal
7. Alan R. Wagner
8. Yoshua Bengio
9. Luke S. Zettlemoyer
10. De-chuan Zhan

Papers from Benoit Steiner's perspective

Ordered by how strongly the researchers Benoit Steiner 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• TensorFlow: A system for large-scale machine learning (2016) — 19,838 citations
• PyTorch: An Imperative Style, High-Performance Deep Learning Library (2019) — 53,912 citations
• Device Placement Optimization with Reinforcement Learning (2017) — 492 citations
• OLLA: Optimizing the Lifetime and Location of Arrays to Reduce the Memory Usage of Neural Networks (2022) — 11 citations
• Studying Large Language Model Generalization with Influence Functions (2023) — 343 citations
• Measuring Faithfulness in Chain-of-Thought Reasoning (2023) — 499 citations
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016) — 11,798 citations