Andreas Steiner
Artificial Intelligence · 2021–2025
A researcher in the Science corpus: 10 papers; trusts 278 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 315 citations into it; #20,079 of 35,345 by trust here, #3,652 by citations
- Computer Vision and Pattern Recognition — 308 citations into it; #14,522 of 21,575 by trust here, #3,222 by citations
- Representation & Self-Supervised Learning — 57 citations into it
- Optimization & Training — 23 citations into it
Whom Andreas Steiner trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Ilya Sutskever
- Diederik P. Kingma
- Christian Szegedy
- Yoshua Bengio
- Ross B. Girshick
- Yann LeCun
- Andrew Zisserman
Who trusts Andreas Steiner overall
- Xiaohua Zhai
- Zach Nussbaum
- Alexander Kolesnikov
- Manoj Kumar
- Mostafa Dehghani
- P. Janson
- Yoshitomo Matsubara
- Basil Mustafa
- Jihao Liu
- Robin Strudel
Papers from Andreas Steiner's perspective
Ordered by how strongly the researchers Andreas Steiner trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
- Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
- How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers (2021) — 854 citations
- LiT: Zero-Shot Transfer with Locked-image text Tuning (2021) — 757 citations
- PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
- SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features (2025) — 1,063 citations
- Gemma 3 Technical Report (2025) — 1,621 citations
- PaliGemma: A versatile 3B VLM for transfer (2024) — 728 citations
- Image Captioners Are Scalable Vision Learners Too (2023)
- Three Towers: Flexible Contrastive Learning with Pretrained Image Models (2023)