Joost R. van Amersfoort

Artificial Intelligence · 2014–2025

A researcher in the Science corpus: 9 papers; trusts 334 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 136 citations into it; #15,349 of 35,345 by trust here, #6,266 by citations
• Computer Vision and Pattern Recognition — 79 citations into it; #13,520 of 21,575 by trust here, #9,079 by citations
• Probabilistic & Bayesian Methods — 34 citations into it
• Safety, Robustness & Interpretability — 29 citations into it
• Generative Models — 28 citations into it; #1,256 of 1,956 by trust here, #1,271 by citations
• Evaluation & Benchmarks — 23 citations into it
• Model Architectures — 21 citations into it
• Optimization & Training — 19 citations into it

Whom Joost R. van Amersfoort trusts overall

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

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

Who trusts Joost R. van Amersfoort overall

1. Moloud Abdar
2. Jishnu Mukhoti
3. Zhen Fang
4. Jeremiah Zhe Liu
5. Yingzhen Li
6. Elizaveta Logacheva
7. Umang Bhatt
8. Zalán Borsos
9. Gustav Eje Henter
10. Samuel Budd

Papers from Joost R. van Amersfoort's perspective

Ordered by how strongly the researchers Joost R. van Amersfoort 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Transformation-Based Models of Video Sequences (2017) — 77 citations
• Variational Recurrent Auto-Encoders (2014) — 269 citations
• BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning (2019) — 777 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• Gemma 2: Improving Open Language Models at a Practical Size (2024) — 2,206 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Deterministic Neural Networks with Appropriate Inductive Biases Capture Epistemic and Aleatoric Uncertainty (2021) — 99 citations
• Simple and Scalable Epistemic Uncertainty Estimation Using a Single Deep Deterministic Neural Network (2020) — 55 citations
• Uncertainty Estimation Using a Single Deep Deterministic Neural Network (2020) — 560 citations