methodology-4
Structured Prediction
3260 papers • 126 benchmarks • 313 datasets
Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search. Source: Torch-Struct: Deep Structured Prediction Library
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Most implemented papers
Convolutional Pose Machines
Yaser Sheikh, S. Wei, V. Ramakrishna, T. Kanade•Fri Jan 29 2016
This work designs a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference in structured prediction tasks such as articulated pose estimation.
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