Tim van Erven
Artificial Intelligence · 2012–2014
A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 32 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 25 citations into it; #21,942 of 35,345 by trust here, #26,682 by citations
- Statistics and Probability — 23 citations into it; #8,032 of 12,932 by trust here, #7,914 by citations
- Management Science and Operations Research — 9 citations into it
- Probabilistic & Bayesian Methods — 9 citations into it; #412 of 1,525 by trust here, #658 by citations
- Computer Networks and Communications — 8 citations into it
- Information Theory — 8 citations into it; #37 of 196 by trust here, #46 by citations
- Statistical Learning Theory — 8 citations into it
- Optimization & Training — 8 citations into it
Whom Tim van Erven trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Tong Zhang
- Peter D. Grünwald
- Yuhong Yang
- Jorma Rissanen
- Andrew R. Barron
- M. Sion
- A. Rényi
- Ofer Shayevitz
- Gustavo L. Gilardoni
- Sida I. Wang
Who trusts Tim van Erven overall
- Yingzhen Li
- Peter D. Grünwald
- Ilya Mironov
- Bai Li
- Mehryar Mohri
- Vitaly Feldman
- Yu-Xiang Wang
- Léonard Blier
- Adeline Langlois
- Changyou Chen
Papers from Tim van Erven's perspective
Ordered by how strongly the researchers Tim van Erven 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
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC–BIC dilemma (2012) — 87 citations
- Rényi Divergence and Kullback-Leibler Divergence (2012) — 1,563 citations
- Follow the Leader with Dropout Perturbations (2014) — 55 citations
- Follow the leader if you can, hedge if you must (2013) — 194 citations