Justin Alsing
Astronomy and Astrophysics · 2011–2019
A researcher in the Science corpus: 7 papers; trusts 52 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Astronomy and Astrophysics — 320 citations into it; #6,887 of 10,964 by trust here, #4,497 by citations
- Nuclear and High Energy Physics — 160 citations into it
- Oceanography — 54 citations into it
- Statistical and Nonlinear Physics — 50 citations into it
- Artificial Intelligence — 27 citations into it; #13,514 of 35,345 by trust here, #17,776 by citations
- Statistics and Probability — 23 citations into it; #5,058 of 12,932 by trust here, #4,371 by citations
- Geophysics — 20 citations into it
- Probabilistic & Bayesian Methods — 17 citations into it; #1,019 of 1,525 by trust here, #960 by citations
Whom Justin Alsing trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Farhan Feroz
- Diederik P. Kingma
- J. Lattimer
- Chad M. Schafer
- Benjamin Dan Wandelt
- George Papamakarios
- A. Steiner
- F. Özel
- J. Brehmer
- Jan-Matthis Lueckmann
Who trusts Justin Alsing overall
- Benjamin Dan Wandelt
- J. Brehmer
- Kyle Cranmer
- George Papamakarios
- Gilles Louppe
- S. Feeney
- R. Brito
- Tom Charnock
- I. Tews
- K. Chatziioannou
Papers from Justin Alsing's perspective
Ordered by how strongly the researchers Justin Alsing trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Dropout: a simple way to prevent neural networks from overfitting
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology (2018) — 155 citations
- Generalized massive optimal data compression (2017) — 90 citations
- Fast likelihood-free cosmology with neural density estimators and active learning (2019) — 214 citations
- Nuisance hardened data compression for fast likelihood-free inference (2019) — 76 citations
- Evidence for a maximum mass cut-off in the neutron star mass distribution and constraints on the equation of state (2017) — 195 citations
- Testing general relativity with present and future astrophysical observations (2015) — 1,331 citations
- Gravitational radiation from compact binary systems in the massive Brans-Dicke theory of gravity (2011) — 165 citations