Ilya Loshchilov
Artificial Intelligence · 2012–2017
A researcher in the Science corpus: 8 papers; trusts 625 researchers and is trusted by 6,620 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 117 citations into it; #269 of 35,345 by trust here, #1,245 by citations
- Optimization & Training — 107 citations into it; #14 of 2,601 by trust here, #25 by citations
- Computer Vision and Pattern Recognition — 65 citations into it; #340 of 21,575 by trust here, #978 by citations
- Computational Mechanics — 20 citations into it; #20 of 10,001 by trust here, #57 by citations
- Model Architectures — 19 citations into it
- Computational Theory and Mathematics — 17 citations into it
- Management Science and Operations Research — 17 citations into it
- Scaling & Learning Theory — 15 citations into it
Whom Ilya Loshchilov trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Diederik P. Kingma
- Geoffrey E. Hinton
- Mike Preuss
- Yoshua Bengio
- Kaiming He
- Jimmy Ba
- Yann LeCun
- Ilya Sutskever
- Gao Huang
Who trusts Ilya Loshchilov overall
- Dan Hendrycks
- Ronghang Hu
- Christoph Feichtenhofer
- Ishan Misra
- Alexei Baevski
- Zhe Gan
- Frank Hutter
- Quoc V. Le
- Yoshua Bengio
- Jianwei Yang
Papers from Ilya Loshchilov's perspective
Ordered by how strongly the researchers Ilya Loshchilov 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)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- SGDR: Stochastic Gradient Descent with Warm Restarts (2016) — 10,851 citations
- A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets (2017) — 772 citations
- Online Batch Selection for Faster Training of Neural Networks (2015) — 351 citations
- CMA-ES for Hyperparameter Optimization of Deep Neural Networks (2016) — 240 citations
- Alternative Restart Strategies for CMA-ES (2012) — 47 citations
- Fixing Weight Decay Regularization in Adam (2017) — 2,716 citations
- Decoupled Weight Decay Regularization (2017) — 36,546 citations
- SGDR: Stochastic Gradient Descent with Restarts (2016) — 481 citations