Dmitry Molchanov

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 294 researchers and is trusted by 111 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #2,306 of 35,345 by trust here, #4,627 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #3,701 of 21,575 by trust here, #4,887 by citations
• Probabilistic & Bayesian Methods — 62 citations into it
• Optimization & Training — 40 citations into it
• Efficiency & Compression — 27 citations into it; #74 of 1,264 by trust here, #282 by citations
• Generative Models — 23 citations into it
• Model Architectures — 19 citations into it
• Statistics and Probability — 13 citations into it

Whom Dmitry Molchanov trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Max Welling
5. Yann LeCun
6. David John Cameron MacKay
7. Yoshua Bengio
8. Ruslan Salakhutdinov
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Dmitry Molchanov overall

1. Christos Louizos
2. Alessandro Achille
3. Max Welling
4. Sidak Pal Singh
5. Kumar Shridhar
6. Jiri Hron
7. Arsenii Ashukha
8. Kirill Neklyudov
9. Diederik P. Kingma
10. Yunhui Guo

Papers from Dmitry Molchanov's perspective

Ordered by how strongly the researchers Dmitry Molchanov 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Variational Dropout Sparsifies Deep Neural Networks (2017) — 897 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
• Doubly Semi-Implicit Variational Inference (2018) — 42 citations
• Bayesian Incremental Learning for Deep Neural Networks (2018) — 24 citations
• Relevance tagging machine (2015)