Erik A. Daxberger

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 203 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #18,829 of 35,345 by trust here, #29,412 by citations
• Probabilistic & Bayesian Methods — 52 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Generative Models — 37 citations into it
• Optimization & Training — 25 citations into it
• Computational Theory and Mathematics — 24 citations into it
• Safety, Robustness & Interpretability — 24 citations into it
• Materials Chemistry — 19 citations into it

Whom Erik A. Daxberger trusts overall

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

1. David John Cameron MacKay
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yann LeCun
5. Yoshua Bengio
6. Alex Krizhevsky
7. Alex Graves
8. Danilo Jimenez Rezende
9. Radford M. Neal
10. Max Welling

Who trusts Erik A. Daxberger overall

1. Austin Tripp
2. Javier Antorán
3. Michael Matena
4. José Miguel Hernández-Lobato
5. Umang Bhatt
6. Colin Raffel
7. Tameem Adel

Papers from Erik A. Daxberger's perspective

Ordered by how strongly the researchers Erik A. Daxberger 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. GENERATIVE ADVERSARIAL NETS

Their papers

• Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining (2020) — 175 citations
• Laplace Redux - Effortless Bayesian Deep Learning (2021) — 467 citations
• Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection (2019) — 69 citations