Malte J. Rasch
Statistics and Probability · 2006–2022
A researcher in the Science corpus: 5 papers; trusts 46 researchers and is trusted by 164 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 58 citations into it; #1,032 of 12,932 by trust here, #458 by citations
- Artificial Intelligence — 53 citations into it; #2,974 of 35,345 by trust here, #3,582 by citations
- Statistical Learning Theory — 52 citations into it; #200 of 1,038 by trust here, #101 by citations
- Computer Vision and Pattern Recognition — 23 citations into it
- Probabilistic & Bayesian Methods — 21 citations into it; #175 of 1,525 by trust here, #93 by citations
- Electrical and Electronic Engineering — 14 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Signal Processing — 11 citations into it; #513 of 7,572 by trust here, #304 by citations
Whom Malte J. Rasch trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Arthur Gretton
- Alex Smola
- Bernhard Schölkopf
- Ingo Steinwart
- Kenji Fukumizu
- Peter J. Bickel
- Francis R. Bach
- Karsten M. Borgwardt
- Jerome H. Friedman
- Michael I. Jordan
Who trusts Malte J. Rasch overall
- Arthur Gretton
- Kenji Fukumizu
- Bharath K. Sriperumbudur
- Mingsheng Long
- Bernhard Schölkopf
- Le Song
- Krikamol Muandet
- Dino Sejdinovic
- Yoshua Bengio
- Aaditya Ramdas
Papers from Malte J. Rasch's perspective
Ordered by how strongly the researchers Malte J. Rasch 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- GENERATIVE ADVERSARIAL NETS
Their papers
- A Kernel Method for the Two-Sample-Problem (2006) — 2,657 citations
- Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006) — 1,690 citations
- A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference (2022) — 314 citations
- A Kernel Two-Sample Test (2012) — 6,578 citations
- A Kernel Approach to Comparing Distributions (2007) — 60 citations