Yaroslav Ganin

Computer Vision and Pattern Recognition · 2014–2022

A researcher in the Science corpus: 9 papers; trusts 924 researchers and is trusted by 5,619 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #365 of 21,575 by trust here, #463 by citations
• Artificial Intelligence — 173 citations into it; #341 of 35,345 by trust here, #609 by citations
• Generative Models — 89 citations into it
• Representation & Self-Supervised Learning — 48 citations into it; #834 of 3,145 by trust here, #179 by citations
• Natural Language Processing — 42 citations into it
• Model Architectures — 35 citations into it
• Optimization & Training — 23 citations into it
• Computational Mechanics — 22 citations into it

Whom Yaroslav Ganin trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yann LeCun
4. Diederik P. Kingma
5. Kaiming He
6. Ilya Sutskever
7. Yoshua Bengio
8. Matthew D. Zeiler
9. Rob Fergus
10. Karen Simonyan

Who trusts Yaroslav Ganin overall

1. Kuniaki Saito
2. Mingsheng Long
3. Judy Hoffman
4. Mohammad Rostami
5. Zhangjie Cao
6. Kate Saenko
7. Trevor Darrell
8. Yoshua Bengio
9. Kaiyang Zhou
10. Jian Liang

Papers from Yaroslav Ganin's perspective

Ordered by how strongly the researchers Yaroslav Ganin trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Domain-Adversarial Training of Neural Networks (2015) — 11,758 citations
• Unsupervised Domain Adaptation by Backpropagation (2014) — 7,030 citations
• Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition (2014) — 952 citations
• Synthesizing Programs for Images using Reinforced Adversarial Learning (2018) — 243 citations
• N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms (2014) — 277 citations
• DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation (2016) — 128 citations
• PolyGen: An Autoregressive Generative Model of 3D Meshes (2020) — 373 citations
• Self-conditioned Embedding Diffusion for Text Generation (2022) — 134 citations
• Continuous diffusion for categorical data (2022) — 209 citations