Kacper P. Chwialkowski
Statistics and Probability · 2014–2016
A researcher in the Science corpus: 5 papers; trusts 62 researchers and is trusted by 36 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 69 citations into it; #1,274 of 12,932 by trust here, #2,309 by citations
- Artificial Intelligence — 50 citations into it; #7,088 of 35,345 by trust here, #17,776 by citations
- Statistical Learning Theory — 40 citations into it; #346 of 1,038 by trust here, #602 by citations
- Probabilistic & Bayesian Methods — 33 citations into it
- Finance — 12 citations into it; #2,377 of 10,518 by trust here, #5,938 by citations
- Statistical and Nonlinear Physics — 10 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Economics and Econometrics — 9 citations into it
Whom Kacper P. Chwialkowski trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Bharath K. Sriperumbudur
- Bernhard Schölkopf
- Kenji Fukumizu
- Xiaofeng Shao
- Alex Smola
- Anne Leucht
- Dino Sejdinovic
- Ingo Steinwart
- Karsten M. Borgwardt
Who trusts Kacper P. Chwialkowski overall
- Qiang Liu
- Krikamol Muandet
- Arthur Gretton
- Kenji Fukumizu
- Yihao Feng
- Balaji Lakshminarayanan
- Bernhard Schölkopf
- Qinyi Zhang
- David Lopez-Paz
- Bharath K. Sriperumbudur
Papers from Kacper P. Chwialkowski's perspective
Ordered by how strongly the researchers Kacper P. Chwialkowski trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- GENERATIVE ADVERSARIAL NETS
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- A Wild Bootstrap for Degenerate Kernel Tests (2014) — 64 citations
- Fast Two-Sample Testing with Analytic Representations of Probability Measures (2015) — 169 citations
- A Kernel Independence Test for Random Processes (2014) — 65 citations
- A Kernel Test of Goodness of Fit (2016) — 370 citations
- Interpretable Distribution Features with Maximum Testing Power (2016) — 148 citations