Predicting Structured Data

By Gökhan H. Bakir, Thomas Hofmann, Bernhard Schölkopf, Taskar B Smola Aj, S. V. N. Vishwanathan. 2007

76 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

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

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Citations in the corpus

Order Matters: Sequence to sequence for sets (2015, neutral): “Unlike traditional structured prediction algorithms (Bakir et al., 2007), our approach relies on the chain rule to serialize output random variables through the strong capabilities of LSTM networks to model long-term correlation.”
From machine learning to machine reasoning (2011, neutral): “Works discussing structured learning systems (e.g., Bakir et al., 2007) often provide more explicit rules.”
An Introduction to Conditional Random Fields (2010, unknown): “papers [4].”
Learning to Localize Objects with Structured Output Regression (2008, methodology): “We learn this mapping in the structured learning framework [14, 15] as g(x) = argmaxy f(x, y) (1)”
Segmenting Retinal Blood Vessels With Deep Neural Networks (2016, background): “To that aim, we resort to a relatively recent machine learning formalism of structured prediction (SP) [34].”
Distributed learning for Random Vector Functional-Link networks (2015, background): “The previous trends also put forth the challenge of analyzing structured and heterogeneous data [4], however, we are not concerned with this issue in this paper.”
Structured Prediction Theory Based on Factor Graph Complexity (2016, unknown): “, 2003, Cortes et al., 2014, Collins, 2001] and do not cover other natural losses such as the edit-distance. They also typically only apply to specific factor graph models. The main exception is the work of McAllester [2007], which provides PAC-Bayesian guarantees for arbitrary losses, though only i”