Marcin Moczulski
Artificial Intelligence · 2014–2016
A researcher in the Science corpus: 4 papers; trusts 218 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 80 citations into it; #27,552 of 35,345 by trust here, #7,728 by citations
- Computer Vision and Pattern Recognition — 62 citations into it; #8,711 of 21,575 by trust here, #5,422 by citations
- Model Architectures — 40 citations into it
- Optimization & Training — 23 citations into it
- Efficiency & Compression — 22 citations into it; #500 of 1,264 by trust here, #429 by citations
- Natural Language Processing — 18 citations into it
- Signal Processing — 12 citations into it
- Computational Mechanics — 9 citations into it; #3,333 of 10,001 by trust here, #991 by citations
Whom Marcin Moczulski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Alex Graves
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
- Quoc V. Le
- Diederik P. Kingma
- Sepp Hochreiter
- Kaiming He
Who trusts Marcin Moczulski overall
- Yu Cheng
- David Balduzzi
- Hesham Mostafa
- Gavia Gray
- Ritchie Zhao
- Duo Wang
- Caglar Gulcehre
- Elliot J. Crowley
- Tri Dao
- Frederick Tung
Papers from Marcin Moczulski's perspective
Ordered by how strongly the researchers Marcin Moczulski trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Going deeper with convolutions
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Deep Fried Convnets (2014) — 274 citations
- Mollifying Networks (2016) — 47 citations
- Noisy Activation Functions (2016) — 306 citations
- ACDC: A Structured Efficient Linear Layer (2015) — 107 citations