Grzegorz Swirszcz

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 8 papers; trusts 155 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 109 citations into it; #27,193 of 35,345 by trust here, #11,624 by citations
• Computer Vision and Pattern Recognition — 43 citations into it; #16,715 of 21,575 by trust here, #11,498 by citations
• Optimization & Training — 38 citations into it; #1,063 of 2,601 by trust here, #1,365 by citations
• Reinforcement Learning — 36 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Electrical and Electronic Engineering — 16 citations into it
• Model Architectures — 16 citations into it
• Signal Processing — 16 citations into it

Whom Grzegorz Swirszcz trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. J. Urgen Schmidhuber
5. Yann LeCun
6. Alex Krizhevsky
7. Ian J. Goodfellow
8. Volodymyr Mnih
9. Ilya Sutskever
10. Alex Graves

Who trusts Grzegorz Swirszcz overall

1. Daniel Jiwoong Im
2. Yu Zhang
3. Luca Venturi
4. Lei Han
5. Jaume Llibre
6. Keerthiram Murugesan
7. Itay Safran
8. Michael Tao
9. Laura Palagi
10. Daniel Soudry

Papers from Grzegorz Swirszcz's perspective

Ordered by how strongly the researchers Grzegorz Swirszcz 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sobolev Training for Neural Networks (2017) — 337 citations
• Distilling Policy Distillation (2019) — 179 citations
• Understanding Synthetic Gradients and Decoupled Neural Interfaces (2017) — 94 citations
• Local minima in training of neural networks (2016) — 82 citations
• Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles (2018) — 18 citations
• Local minima in training of deep networks (2017) — 44 citations
• Multi-level Lasso for Sparse Multi-task Regression (2012) — 103 citations
• On the limit cycles of polynomial vector field (2011) — 47 citations