Jonathan S. Yedidia
Probabilistic & Bayesian Methods · 1991–2016
A researcher in the Science corpus: 7 papers; trusts 41 researchers and is trusted by 105 researchers here, as inferred from the citation graph.
Subfields
- Probabilistic & Bayesian Methods — 33 citations into it; #163 of 1,525 by trust here, #160 by citations
- Artificial Intelligence — 25 citations into it; #4,138 of 35,345 by trust here, #7,010 by citations
- Computer Networks and Communications — 15 citations into it; #592 of 9,886 by trust here, #891 by citations
- Optimization & Training — 9 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
- Electrical and Electronic Engineering — 5 citations into it; #13,142 of 16,539 by trust here, #16,053 by citations
- Model Architectures — 5 citations into it
Whom Jonathan S. Yedidia trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Lawrence R. Rabiner
- Tom Minka
- Brendan J. Frey
- Geoffrey E. Hinton
- Michael I. Jordan
- von Madeleine Leidheiser
- Radford M. Neal
- Yair Weiss
- William T. Freeman
- Martin J. Wainwright
Who trusts Jonathan S. Yedidia overall
- Martin J. Wainwright
- Ruslan Salakhutdinov
- Charles Sutton
- Tommi S. Jaakkola
- William T. Freeman
- Zhuowen Tu
- Alan L. Yuille
- Yoshua Bengio
- Xiaogang Wang
- Qiang Liu
Papers from Jonathan S. Yedidia's perspective
Ordered by how strongly the researchers Jonathan S. Yedidia 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Constructing free-energy approximations and generalized belief propagation algorithms (2005) — 1,756 citations
- Assumed Density Filtering Methods for Learning Bayesian Neural Networks (2016) — 56 citations
- Rateless codes on noisy channels (2004) — 335 citations
- Understanding belief propagation and its generalizations (2003) — 1,702 citations
- Bethe free energy, Kikuchi approximations, and belief propagation algorithms (2001) — 244 citations
- Generalized Belief Propagation (2000) — 1,230 citations
- How to expand around mean-field theory using high-temperature expansions (1991)