Nick Pawlowski

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 202 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #16,088 of 35,345 by trust here, #15,004 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #10,680 of 21,575 by trust here, #9,079 by citations
• Reinforcement Learning — 35 citations into it
• Generative Models — 31 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Biophysics — 16 citations into it
• Model Architectures — 13 citations into it
• Natural Language Processing — 12 citations into it; #5,029 of 5,688 by trust here, #4,682 by citations

Whom Nick Pawlowski trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Alex Krizhevsky
7. Danilo Jimenez Rezende
8. Jimmy Ba
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Nick Pawlowski overall

1. Arvid Lundervold
2. Neale Ratzlaff
3. G. Oren
4. Christian Henning
5. Oscar Chang
6. Murat Sensoy
7. Zhenyi Wang
8. Johannes von Oswald
9. Fernando Pérez-García
10. Peter Goldsborough

Papers from Nick Pawlowski's perspective

Ordered by how strongly the researchers Nick Pawlowski 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Implicit Weight Uncertainty in Neural Networks (2017) — 100 citations
• Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning (2017) — 103 citations
• Rasa: Open Source Language Understanding and Dialogue Management (2017) — 487 citations
• Deep Structural Causal Models for Tractable Counterfactual Inference (2020) — 331 citations
• CytoGAN: Generative Modeling of Cell Images (2017) — 72 citations
• Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation (2017) — 460 citations
• DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images (2017) — 85 citations
• Automating Morphological Profiling with Generic Deep Convolutional Networks (2016) — 90 citations