Anthony L. Caterini
Artificial Intelligence · 2018–2019
A researcher in the Science corpus: 2 papers; trusts 88 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 31 citations into it; #28,088 of 35,345 by trust here, #32,362 by citations
- Generative Models — 30 citations into it
- Computer Vision and Pattern Recognition — 29 citations into it; #16,987 of 21,575 by trust here, #20,271 by citations
- Probabilistic & Bayesian Methods — 23 citations into it; #1,319 of 1,525 by trust here, #1,415 by citations
- Statistical and Nonlinear Physics — 9 citations into it; #7,603 of 10,127 by trust here, #8,981 by citations
- Model Architectures — 6 citations into it
- Optimization & Training — 5 citations into it
- Statistics and Probability — 5 citations into it
Whom Anthony L. Caterini trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Radford M. Neal
- Danilo Jimenez Rezende
- Alex Krizhevsky
- Geoffrey E. Hinton
- Ruslan Salakhutdinov
- Max Welling
- Kaiming He
- Yann LeCun
- Matthew D. Hoffman
Who trusts Anthony L. Caterini overall
Papers from Anthony L. Caterini's perspective
Ordered by how strongly the researchers Anthony L. Caterini trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Hamiltonian Variational Auto-Encoder (2018) — 103 citations
- Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows (2019) — 146 citations