Michal Lukasik

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Natural Language Processing — 10 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Model Architectures — 5 citations into it

Whom Michal Lukasik trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Christian Szegedy
4. Alex Krizhevsky
5. Yoshua Bengio
6. A. P. Dawid
7. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. Oriol Vinyals

Who trusts Michal Lukasik overall

1. Pengfei Chen
2. Fabio Cermelli
3. Taehyeon Kim
4. Guangyong Chen
5. Dario Fontanel

Papers from Michal Lukasik's perspective

Ordered by how strongly the researchers Michal Lukasik 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Does label smoothing mitigate label noise? (2020) — 424 citations
• Metrics for Evaluation of Word-level Machine Translation Quality Estimation (2016) — 6 citations