Joeri Hermans

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 126 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #24,525 of 35,345 by trust here, #25,809 by citations
• Probabilistic & Bayesian Methods — 72 citations into it; #1,061 of 1,525 by trust here, #1,244 by citations
• Astronomy and Astrophysics — 59 citations into it
• Statistics and Probability — 55 citations into it; #10,079 of 12,932 by trust here, #11,487 by citations
• Computer Vision and Pattern Recognition — 41 citations into it
• Nuclear and High Energy Physics — 36 citations into it
• Generative Models — 28 citations into it
• Statistical and Nonlinear Physics — 25 citations into it; #7,429 of 10,127 by trust here, #9,324 by citations

Whom Joeri Hermans trusts overall

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

1. Radford M. Neal
2. David John Cameron MacKay
3. Diederik P. Kingma
4. Michael U Gutmann
5. Kyle Cranmer
6. George Papamakarios
7. Geoffrey E. Hinton
8. Vivek S. Borkar
9. Gilles Louppe
10. Donald B. Rubin

Who trusts Joeri Hermans overall

1. Kyle Cranmer
2. J. Brehmer
3. Gilles Louppe
4. Owen Thomas
5. Álvaro Tejero-Cantero
6. Nilanjan Banik
7. Ritabrata Dutta

Papers from Joeri Hermans's perspective

Ordered by how strongly the researchers Joeri Hermans 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Likelihood-free MCMC with Approximate Likelihood Ratios (2019) — 21 citations
• Towards constraining warm dark matter with stellar streams through neural simulation-based inference (2020) — 32 citations
• Likelihood-free MCMC with Amortized Approximate Ratio Estimators (2019) — 270 citations
• Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019) — 80 citations