Igor Melnyk

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 68 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #27,737 of 35,345 by trust here, #17,776 by citations
• Natural Language Processing — 29 citations into it; #4,572 of 5,688 by trust here, #4,250 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Reinforcement Learning — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Applied Mathematics — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it
• Generative Models — 5 citations into it

Whom Igor Melnyk trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Ilya Sutskever
6. Yann LeCun
7. Richard Socher
8. Jeffrey Pennington
9. Alex Krizhevsky
10. Dzmitry Bahdanau

Who trusts Igor Melnyk overall

1. Zhiqiang Hu
2. Leo Laugier
3. Ning Dai
4. Di Jin

Papers from Igor Melnyk's perspective

Ordered by how strongly the researchers Igor Melnyk 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer (2018) — 170 citations
• ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models (2021) — 30 citations
• Wasserstein Barycenter Model Ensembling (2019) — 27 citations
• Estimating Information Flow in Neural Networks (2018) — 32 citations