Ivan Kobyzev

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 282 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 300 citations into it; #27,874 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 110 citations into it; #17,089 of 21,575 by trust here, #10,677 by citations
• Statistical and Nonlinear Physics — 101 citations into it
• Graph Learning — 81 citations into it
• Model Architectures — 53 citations into it
• Representation & Self-Supervised Learning — 45 citations into it
• Probabilistic & Bayesian Methods — 38 citations into it
• Natural Language Processing — 37 citations into it

Whom Ivan Kobyzev trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Max Welling
5. Kaiming He
6. Alex Krizhevsky
7. Ilya Sutskever
8. Laurent Dinh
9. Yann LeCun
10. Danilo Jimenez Rezende

Who trusts Ivan Kobyzev overall

1. Derek Onken
2. Rameen Abdal
3. Oleksiy Ostapenko
4. Oliver Dürr
5. Michael Dorkenwald
6. Qingsong Wen
7. Samy Wu Fung
8. Peihao Zhu
9. Pau Rodr'iguez L'opez
10. Stefan Hörtling

Papers from Ivan Kobyzev's perspective

Ordered by how strongly the researchers Ivan Kobyzev 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Normalizing Flows: An Introduction and Review of Current Methods (2020) — 1,546 citations
• Representation Learning for Dynamic Graphs: A Survey (2019) — 638 citations
• DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation (2022) — 309 citations
• Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019) — 46 citations