Dino Sejdinovic
Artificial Intelligence · 2012–2018
A researcher in the Science corpus: 18 papers; trusts 212 researchers and is trusted by 65 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 209 citations into it; #3,750 of 35,345 by trust here, #5,638 by citations
- Statistics and Probability — 194 citations into it; #923 of 12,932 by trust here, #795 by citations
- Statistical Learning Theory — 129 citations into it; #221 of 1,038 by trust here, #208 by citations
- Probabilistic & Bayesian Methods — 118 citations into it; #639 of 1,525 by trust here, #658 by citations
- Computer Vision and Pattern Recognition — 39 citations into it; #16,986 of 21,575 by trust here, #20,271 by citations
- Statistical and Nonlinear Physics — 39 citations into it; #7,602 of 10,127 by trust here, #8,981 by citations
- Causal Inference & Discovery — 36 citations into it
- Signal Processing — 25 citations into it
Whom Dino Sejdinovic trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Bernhard Schölkopf
- Kenji Fukumizu
- Bharath K. Sriperumbudur
- Alex Smola
- Francis R. Bach
- Radford M. Neal
- Michael I. Jordan
- Christopher K. I. Williams
- G'abor J. Sz'ekely
Who trusts Dino Sejdinovic overall
- Arthur Gretton
- Krikamol Muandet
- Kacper P. Chwialkowski
- Mingsheng Long
- Bharath K. Sriperumbudur
- Kenji Fukumizu
- Bernhard Schölkopf
- Praneeth Vepakomma
- Heiko Strathmann
- Aaditya Ramdas
Papers from Dino Sejdinovic's perspective
Ordered by how strongly the researchers Dino Sejdinovic 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
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Equivalence of distance-based and RKHS-based statistics in hypothesis testing (2012) — 794 citations
- A Wild Bootstrap for Degenerate Kernel Tests (2014) — 64 citations
- Fast Two-Sample Testing with Analytic Representations of Probability Measures (2015) — 169 citations
- Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages (2015) — 33 citations
- Hamiltonian Variational Auto-Encoder (2018) — 103 citations
- A Kernel Test for Three-Variable Interactions (2013) — 42 citations
- Kernel Adaptive Metropolis-Hastings (2013) — 47 citations
- Bayesian Learning of Kernel Embeddings (2016) — 48 citations
- Kernel Sequential Monte Carlo (2015) — 8 citations
- K2-ABC: Approximate Bayesian Computation with Infinite Dimensional Summary Statistics via Kernel Embeddings (2015) — 10 citations
- Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families (2015) — 78 citations
- Hypothesis testing using pairwise distances and associated kernels (2012) — 42 citations
- Large-scale kernel methods for independence testing (2016) — 144 citations
- Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences (2018) — 431 citations
- Causal Inference via Kernel Deviance Measures (2018) — 68 citations
- Detecting and quantifying causal associations in large nonlinear time series datasets (2017) — 1,071 citations
- Kernel Adaptive Metropolis-Hastings (2013)
- Optimal kernel choice for large-scale two-sample tests (2012) — 764 citations