Max Vladymyrov

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 92 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 138 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it

Whom Max Vladymyrov trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Kaiming He
6. J. Urgen Schmidhuber
7. Christian Szegedy
8. Ilya Sutskever
9. Yann LeCun
10. Alex Graves

Papers from Max Vladymyrov's perspective

Ordered by how strongly the researchers Max Vladymyrov 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Fine-tuning Image Transformers using Learnable Memory (2022) — 57 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Transformers learn in-context by gradient descent (2022) — 821 citations