Arsenii Ashukha

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 327 researchers and is trusted by 111 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #3,637 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 106 citations into it; #2,303 of 35,345 by trust here, #4,627 by citations
• Generative Models — 44 citations into it
• Optimization & Training — 39 citations into it
• Probabilistic & Bayesian Methods — 39 citations into it
• Efficiency & Compression — 25 citations into it; #73 of 1,264 by trust here, #282 by citations
• Model Architectures — 25 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Arsenii Ashukha trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Max Welling
5. Yann LeCun
6. Kaiming He
7. Yoshua Bengio
8. Christian Szegedy
9. Ilya Sutskever
10. David John Cameron MacKay

Who trusts Arsenii Ashukha overall

1. Christos Louizos
2. Alessandro Achille
3. Max Welling
4. Kumar Shridhar
5. Jiri Hron
6. Sidak Pal Singh
7. Kirill Neklyudov
8. Felix Laumann
9. Diederik P. Kingma
10. Maximilian Pichler

Papers from Arsenii Ashukha's perspective

Ordered by how strongly the researchers Arsenii Ashukha 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
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

• Variational Dropout Sparsifies Deep Neural Networks (2017) — 897 citations
• Resolution-robust Large Mask Inpainting with Fourier Convolutions (2021) — 1,391 citations
• Structured Bayesian Pruning via Log-Normal Multiplicative Noise (2017) — 193 citations
• Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning (2020) — 362 citations
• Variance Networks: When Expectation Does Not Meet Your Expectations (2018) — 25 citations
• Bayesian Incremental Learning for Deep Neural Networks (2018) — 24 citations