Proximal Deep Structured Models

By Shenlong Wang, Sanja Fidler, Raquel Urtasun. 2016

88 citations recorded by the source corpus.

Subfields: Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

1. Kevin Gimpel
2. Lifu Tu
3. Yoon Kim
4. Alexander M. Rush
5. Bishan Yang
6. Luong Hoang
7. Carl Denton
8. Andrew McCallum

Citations in the corpus

• Structured Attention Networks (2017, methodology): “Many optimization algorithms (e.g. gradient descent, LBFGS) are also differentiable (Domke, 2012; Maclaurin et al., 2015), and have been used as output layers for structured prediction in energy-based models (Belanger & McCallum, 2016; Wang et al., 2016).”
• Recurrent Inference Machines for Solving Inverse Problems (2017, methodology): “This insight has proven successful in several image restoration tasks [10, 12, 14] in First, it burdens practitioners with the choice of appropriate prior and inference models for the given data and task at hand, which typically requires a lot of expert knowledge from practitioners.”
• Learning Approximate Inference Networks for Structured Prediction (2018, background): “Since we relax discrete output variables to be continuous, there is also a connection to recent work that focuses on structured prediction with continuous valued output variables (Wang et al., 2016).”
• End-to-End Learning for Structured Prediction Energy Networks (2017, methodology): “Our data processing and hyperparam-eters are designed to replicate the setup of Wang et al. (2016), who demonstrate state-of-the art results for energy-minimization-based denoising on the dataset.”
• ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing (2017, background): “The tremendous success of deep learning for many image processing applications has also led researchers to consider relating iterative optimization methods to neural networks [16, 39, 43, 32].”
• Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (2017, methodology): “For the sake of completeness, we have to mention methods such as [41] who apply the contrary approach and use variational methods as boilerplate models to design their network architecture.”