Stanislav Pidhorskyi

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 298 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 62 citations into it; #6,505 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 48 citations into it; #22,737 of 35,345 by trust here, #20,836 by citations
• Generative Models — 42 citations into it; #620 of 1,956 by trust here, #1,200 by citations
• Representation & Self-Supervised Learning — 9 citations into it
• Optimization & Training — 7 citations into it
• Signal Processing — 7 citations into it
• Statistical and Nonlinear Physics — 6 citations into it
• Computational Mechanics — 5 citations into it

Whom Stanislav Pidhorskyi trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Kaiming He
7. Ian J. Goodfellow
8. Karen Simonyan
9. Ilya Sutskever
10. Individualized Treat

Who trusts Stanislav Pidhorskyi overall

1. Yuval Alaluf
2. Yotam Nitzan
3. Zongze Wu
4. Or Patashnik
5. Jingkang Yang
6. Yang Zhao
7. Poojan Oza
8. Zhisheng Xiao
9. Faruk Ahmed
10. Daniel Cohen-Or

Papers from Stanislav Pidhorskyi's perspective

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

Their papers

• Adversarial Latent Autoencoders (2020) — 289 citations
• Generative Probabilistic Novelty Detection with Adversarial Autoencoders (2018) — 349 citations