Matthias Bauer

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 6 papers; trusts 439 researchers and is trusted by 50 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 145 citations into it; #6,190 of 35,345 by trust here, #11,921 by citations
• Computer Vision and Pattern Recognition — 76 citations into it
• Probabilistic & Bayesian Methods — 65 citations into it; #502 of 1,525 by trust here, #558 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Optimization & Training — 32 citations into it
• Generative Models — 24 citations into it; #555 of 1,956 by trust here, #1,549 by citations
• Safety, Robustness & Interpretability — 22 citations into it
• Model Architectures — 19 citations into it

Whom Matthias Bauer trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Radford M. Neal
5. Yoshua Bengio
6. Yann LeCun
7. Ruslan Salakhutdinov
8. Kaiming He
9. Ilya Sutskever
10. Alex Graves

Who trusts Matthias Bauer overall

1. Arash Vahdat
2. Thang D. Bui
3. Erik A. Daxberger
4. Jonathan Gordon
5. Laurent Dinh
6. Jyoti Aneja
7. John F. Bronskill
8. Martin Mundt
9. Dieterich Lawson
10. George Papamakarios

Papers from Matthias Bauer's perspective

Ordered by how strongly the researchers Matthias Bauer 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Discriminative k-shot learning using probabilistic models (2017) — 71 citations
• Resampled Priors for Variational Autoencoders (2018) — 122 citations
• Understanding Probabilistic Sparse Gaussian Process Approximations (2016) — 286 citations
• Laplace Redux - Effortless Bayesian Deep Learning (2021) — 467 citations
• Interpretable and Differentially Private Predictions (2019) — 63 citations
• Meta-Learning Probabilistic Inference for Prediction (2018) — 289 citations