Structured Prediction
186 papers with code • 1 benchmarks • 6 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.
Libraries
Use these libraries to find Structured Prediction models and implementations
MNIST
ListOps
CFQ
SciREX
WIKIOG
WBM
Latest papers with no code
Training Multimedia Event Extraction With Generated Images and Captions
Contemporary news reporting increasingly features multimedia content, motivating research on multimedia event extraction.
On Certified Generalization in Structured Prediction
In structured prediction, target objects have rich internal structure which does not factorize into independent components and violates common i. i. d.
Partial Inference in Structured Prediction
In this paper, we examine the problem of partial inference in the context of structured prediction.
Computing a partition function of a generalized pattern-based energy over a semiring
For a general language $\Gamma$ and non-positive weights, the minimization task can be carried out in ${\mathcal O}(|V|\cdot |\overline{\Gamma^{\cap}}|^2)$ time.
Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective
We show that these exhaustive comparisons can be avoided, and, moreover, the complexity of such tasks can be reduced to linear by casting the relation between tokens as a partial order over the string.
Modified Gauss-Newton Algorithms under Noise
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing.
Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels
Surrogate kernel-based methods offer a flexible solution to structured output prediction by leveraging the kernel trick in both input and output spaces.
Exact Inference in High-order Structured Prediction
In this paper, we study the problem of inference in high-order structured prediction tasks.
Backpropagation of Unrolled Solvers with Folded Optimization
A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form.
On the inconsistency of separable losses for structured prediction
In this paper, we prove that separable negative log-likelihood losses for structured prediction are not necessarily Bayes consistent, or, in other words, minimizing these losses may not result in a model that predicts the most probable structure in the data distribution for a given input.
