Tim Dockhorn

Computer Vision and Pattern Recognition · 2021–2025

A researcher in the Science corpus: 7 papers; trusts 583 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 328 citations into it; #7,171 of 21,575 by trust here, #2,729 by citations
• Generative Models — 219 citations into it; #939 of 1,956 by trust here, #1,336 by citations
• Artificial Intelligence — 123 citations into it
• Statistical and Nonlinear Physics — 78 citations into it; #3,846 of 10,127 by trust here, #3,735 by citations
• Multimodal Learning — 49 citations into it
• Probabilistic & Bayesian Methods — 39 citations into it; #904 of 1,525 by trust here, #878 by citations
• Computer Graphics and Computer-Aided Design — 37 citations into it
• Signal Processing — 34 citations into it

Whom Tim Dockhorn trusts overall

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

1. Radford M. Neal
2. Jonathan Ho
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Aapo Hyvärinen
6. Yoshua Bengio
7. Yang Song
8. Pascal Vincent
9. Alex Krizhevsky
10. Kaiming He

Who trusts Tim Dockhorn overall

1. Michael S. Albergo
2. Qinsheng Zhang
3. Jaehyeong Jo
4. Tero Karras
5. Nanye Ma
6. Ye Yuan
7. Florinel-Alin Croitoru
8. Bowen Jing
9. Dongjun Kim
10. Wenliang Zhao

Papers from Tim Dockhorn's perspective

Ordered by how strongly the researchers Tim Dockhorn 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. GENERATIVE ADVERSARIAL NETS

Their papers

• Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models (2023) — 1,730 citations
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (2023) — 5,094 citations
• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets (2023) — 2,748 citations
• Score-Based Generative Modeling with Critically-Damped Langevin Diffusion (2021) — 286 citations
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (2024) — 4,349 citations
• Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation (2024) — 299 citations
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space (2025) — 917 citations