Stanislaw Jastrzebski

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 12 papers; trusts 350 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #6,864 of 35,345 by trust here, #1,641 by citations
• Computer Vision and Pattern Recognition — 105 citations into it; #7,474 of 21,575 by trust here, #2,191 by citations
• Optimization & Training — 95 citations into it; #2,306 of 2,601 by trust here, #887 by citations
• Natural Language Processing — 65 citations into it; #2,050 of 5,688 by trust here, #746 by citations
• Representation & Self-Supervised Learning — 53 citations into it; #2,945 of 3,145 by trust here, #2,910 by citations
• Scaling & Learning Theory — 53 citations into it
• Model Architectures — 49 citations into it
• Computational Theory and Mathematics — 35 citations into it

Whom Stanislaw Jastrzebski trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Diederik P. Kingma
7. Karen Simonyan
8. Ilya Sutskever
9. Sepp Hochreiter
10. Ian J. Goodfellow

Who trusts Stanislaw Jastrzebski overall

1. Stanislav Fort
2. Leslie N. Smith
3. Yoshua Bengio
4. Jeremy M. Cohen
5. Jonas Pfeiffer
6. Seyed Iman Mirzadeh
7. Zixuan Ke
8. Frederik Benzing
9. Charles H. Martin
10. Sandeep Subramanian

Papers from Stanislaw Jastrzebski's perspective

Ordered by how strongly the researchers Stanislaw Jastrzebski 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Parameter-Efficient Transfer Learning for NLP (2019) — 6,877 citations
• A Closer Look at Memorization in Deep Networks (2017) — 2,230 citations
• Residual Connections Encourage Iterative Inference (2017) — 182 citations
• On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length (2018) — 145 citations
• Stiffness: A New Perspective on Generalization in Neural Networks (2019) — 109 citations
• The Break-Even Point on Optimization Trajectories of Deep Neural Networks (2020) — 203 citations
• Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening (2019) — 596 citations
• How to evaluate word embeddings? On importance of data efficiency and simple supervised tasks (2017) — 79 citations
• Learning to Compute Word Embeddings On the Fly (2017) — 86 citations
• Three Factors Influencing Minima in SGD (2017) — 546 citations
• Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening (2020) — 26 citations
• Large Scale Structure of Neural Network Loss Landscapes (2019) — 106 citations