Janne Hellsten

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 324 researchers and is trusted by 1,017 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 192 citations into it; #1,342 of 21,575 by trust here, #1,172 by citations
• Generative Models — 117 citations into it; #154 of 1,956 by trust here, #141 by citations
• Artificial Intelligence — 90 citations into it
• Optimization & Training — 42 citations into it
• Model Architectures — 23 citations into it
• Computational Mechanics — 16 citations into it
• Media Technology — 13 citations into it
• Biophysics — 12 citations into it

Whom Janne Hellsten trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Tero Karras
5. Kaiming He
6. Yoshua Bengio
7. Yann LeCun
8. Ilya Sutskever
9. Karen Simonyan
10. Jimmy Ba

Who trusts Janne Hellsten overall

1. Axel Sauer
2. Yang Song
3. Tero Karras
4. Jun-Yan Zhu
5. Richard Zhang
6. Or Patashnik
7. Eli Shechtman
8. Yuval Alaluf
9. Zongze Wu
10. Peihao Zhu

Papers from Janne Hellsten's perspective

Ordered by how strongly the researchers Janne Hellsten 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. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Analyzing and Improving the Image Quality of StyleGAN (2019) — 7,107 citations
• Training Generative Adversarial Networks with Limited Data (2020) — 2,283 citations
• Alias-Free Generative Adversarial Networks (2021) — 2,077 citations
• Modular primitives for high-performance differentiable rendering (2020) — 656 citations
• Analyzing and Improving the Training Dynamics of Diffusion Models (2023) — 442 citations