Ivan Rubachev

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 4 papers; trusts 196 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 100 citations into it; #20,827 of 35,345 by trust here, #15,542 by citations
• Computer Vision and Pattern Recognition — 83 citations into it; #12,960 of 21,575 by trust here, #10,500 by citations
• Generative Models — 38 citations into it
• Model Architectures — 31 citations into it
• Optimization & Training — 25 citations into it
• Signal Processing — 14 citations into it
• Statistical and Nonlinear Physics — 14 citations into it
• Biophysics — 8 citations into it

Whom Ivan Rubachev trusts overall

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

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

Who trusts Ivan Rubachev overall

1. Léo Grinsztajn
2. Julia Wolleb
3. C. Yuhua
4. Edouard Oyallon
5. Akim Kotelnikov
6. Songqiao Han
7. Alexandros Graikos

Papers from Ivan Rubachev's perspective

Ordered by how strongly the researchers Ivan Rubachev 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Label-Efficient Semantic Segmentation with Diffusion Models (2021) — 723 citations
• Revisiting Deep Learning Models for Tabular Data (2021) — 1,551 citations
• On Embeddings for Numerical Features in Tabular Deep Learning (2022) — 308 citations
• TabDDPM: Modelling Tabular Data with Diffusion Models (2022) — 613 citations