Justin Domke
Computer Vision and Pattern Recognition · 2008–2019
A researcher in the Science corpus: 7 papers; trusts 381 researchers and is trusted by 221 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 41 citations into it; #3,139 of 21,575 by trust here, #6,873 by citations
- Artificial Intelligence — 39 citations into it; #1,737 of 35,345 by trust here, #7,531 by citations
- Probabilistic & Bayesian Methods — 37 citations into it; #167 of 1,525 by trust here, #454 by citations
- Statistics and Probability — 13 citations into it
- Generative Models — 10 citations into it
- Computational Mechanics — 7 citations into it; #1,008 of 10,001 by trust here, #3,190 by citations
- Optimization & Training — 6 citations into it; #578 of 2,601 by trust here, #1,448 by citations
Whom Justin Domke trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Navneet Dalal
- Martin J. Wainwright
- Bill Triggs
- Geoffrey E. Hinton
- Yann LeCun
- Julian Besag
- Boris Polyak
- Tom Minka
- Kevin P. Murphy
- Hugo Larochelle
Who trusts Justin Domke overall
- David Belanger
- Dougal Maclaurin
- Yoon Kim
- Ke Li
- Martin Kiefel
- John R. Hershey
- Philemon Brakel
- Jitendra Malik
- Shinji Watanabe
- Fabian Pedregosa
Papers from Justin Domke's perspective
Ordered by how strongly the researchers Justin Domke trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Parameter learning with truncated message-passing (2011) — 53 citations
- Finito: A faster, permutable incremental gradient method for big data problems (2014) — 172 citations
- Who killed the directed model? (2008) — 33 citations
- Learning Graphical Model Parameters with Approximate Marginal Inference (2013) — 188 citations
- Importance Weighting and Variational Inference (2018) — 120 citations
- Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation (2019) — 21 citations
- Generic Methods for Optimization-Based Modeling (2012) — 418 citations