Basil Mustafa

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 17 papers; trusts 829 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 544 citations into it; #12,478 of 35,345 by trust here, #2,203 by citations
• Computer Vision and Pattern Recognition — 517 citations into it; #9,188 of 21,575 by trust here, #2,191 by citations
• Representation & Self-Supervised Learning — 148 citations into it; #3,103 of 3,145 by trust here, #1,159 by citations
• Evaluation & Benchmarks — 131 citations into it
• Multimodal Learning — 111 citations into it; #638 of 1,211 by trust here, #224 by citations
• Large Language Models — 87 citations into it; #525 of 799 by trust here, #325 by citations
• Model Architectures — 79 citations into it
• Natural Language Processing — 75 citations into it

Whom Basil Mustafa trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Jia Deng
10. Karen Simonyan

Who trusts Basil Mustafa overall

1. William Fedus
2. Mitchell Wortsman
3. Tal Ridnik
4. Joan Puigcerver
5. Yuanhan Zhang
6. Matthias Minderer
7. Moo Jin Kim
8. Barret Zoph
9. Shengbang Tong
10. Bo Li

Papers from Basil Mustafa's perspective

Ordered by how strongly the researchers Basil Mustafa 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022) — 1,015 citations
• Scaling Vision Transformers to 22 Billion Parameters (2023) — 911 citations
• LiT: Zero-Shot Transfer with Locked-image text Tuning (2021) — 757 citations
• Scaling Vision with Sparse Mixture of Experts (2021) — 1,110 citations
• Sigmoid Loss for Language Image Pre-Training (2023) — 3,447 citations
• PaLI-X: On Scaling up a Multilingual Vision and Language Model (2023) — 301 citations
• Scalable Transfer Learning with Expert Models (2020) — 72 citations
• Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts (2022) — 341 citations
• Big Self-Supervised Models Advance Medical Image Classification (2021) — 741 citations
• Deep Ensembles for Low-Data Transfer Learning (2020) — 27 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 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
• Capabilities of Gemini Models in Medicine (2024) — 407 citations
• From Sparse to Soft Mixtures of Experts (2023) — 306 citations
• Three Towers: Flexible Contrastive Learning with Pretrained Image Models (2023)
• Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints (2022) — 215 citations