Krzysztof Chalupka

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 7 papers; trusts 147 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #15,932 of 35,345 by trust here, #16,471 by citations
• Causal Inference & Discovery — 36 citations into it; #329 of 364 by trust here, #266 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #13,156 of 21,575 by trust here, #12,992 by citations
• Statistics and Probability — 26 citations into it; #10,174 of 12,932 by trust here, #11,487 by citations
• Probabilistic & Bayesian Methods — 25 citations into it
• Optimization & Training — 13 citations into it; #2,595 of 2,601 by trust here, #2,259 by citations
• Evaluation & Benchmarks — 9 citations into it
• Statistical Learning Theory — 9 citations into it; #905 of 1,038 by trust here, #1,006 by citations

Whom Krzysztof Chalupka trusts overall

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

1. Cosma Rohilla Shalizi
2. Tom L. Burr
3. Geoffrey E. Hinton
4. Christopher K. I. Williams
5. Bernhard Schölkopf
6. Alex Krizhevsky
7. Yoshua Bengio
8. Jan Peters
9. Yann LeCun
10. Dominik Janzing

Who trusts Krzysztof Chalupka overall

1. Bernhard Schölkopf
2. Yoshua Bengio
3. David Lopez-Paz
4. Akanksha Atrey
5. Francesco Locatello
6. Mohammad Taha Bahadori
7. Atticus Geiger
8. Xinting Hu
9. Alhussein Fawzi
10. Frederick Eberhardt

Papers from Krzysztof Chalupka's perspective

Ordered by how strongly the researchers Krzysztof Chalupka 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visual Causal Feature Learning (2014) — 160 citations
• Estimating Causal Direction and Confounding of Two Discrete Variables (2016) — 8 citations
• Fast Conditional Independence Test for Vector Variables with Large Sample Sizes (2018) — 37 citations
• Causal feature learning: an overview (2017) — 104 citations
• Causal Regularization (2019) — 57 citations
• Causal Regularization (2017)
• Multi-Level Cause-Effect Systems (2015) — 64 citations