Mojm'ir Mutn'y
Artificial Intelligence · 2020
A researcher in the Science corpus: 1 paper; trusts 55 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #19,952 of 35,345 by trust here, #18,586 by citations
- Computer Vision and Pattern Recognition — 20 citations into it
- Optimization & Training — 14 citations into it; #1,703 of 2,601 by trust here, #1,638 by citations
- Meta & Continual Learning — 8 citations into it; #631 of 1,041 by trust here, #681 by citations
- Model Architectures — 7 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
- Computational Mechanics — 6 citations into it; #5,396 of 10,001 by trust here, #4,039 by citations
- Computational Theory and Mathematics — 5 citations into it
Whom Mojm'ir Mutn'y trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Fabian Pedregosa
- Ilya Sutskever
- Karen Simonyan
- Radford M. Neal
Who trusts Mojm'ir Mutn'y overall
Papers from Mojm'ir Mutn'y's perspective
Ordered by how strongly the researchers Mojm'ir Mutn'y trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Coresets via Bilevel Optimization for Continual Learning and Streaming (2020) — 307 citations