Alexander Immer

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 51 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 86 citations into it; #25,080 of 35,345 by trust here, #13,962 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Probabilistic & Bayesian Methods — 39 citations into it
• Optimization & Training — 29 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Model Architectures — 13 citations into it
• Statistical and Nonlinear Physics — 10 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Alexander Immer trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Radford M. Neal
2. Diederik P. Kingma
3. Yann LeCun
4. Geoffrey E. Hinton
5. David John Cameron MacKay
6. Yoshua Bengio
7. L. Eon Bottou
8. Mohammad Emtiyaz Khan
9. Alex Krizhevsky
10. Alex Graves

Who trusts Alexander Immer overall

1. Fei Ye
2. Adrian G. Bors

Papers from Alexander Immer's perspective

Ordered by how strongly the researchers Alexander Immer trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Continual Deep Learning by Functional Regularisation of Memorable Past (2020) — 174 citations
• Laplace Redux - Effortless Bayesian Deep Learning (2021) — 467 citations
• Approximate Inference Turns Deep Networks into Gaussian Processes (2019) — 141 citations