Zobrazeno 1 - 10
of 8 391
pro vyhledávání: '"Structured prediction"'
Conformal prediction has recently emerged as a promising strategy for quantifying the uncertainty of a predictive model; these algorithms modify the model to output sets of labels that are guaranteed to contain the true label with high probability. H
Externí odkaz:
http://arxiv.org/abs/2410.06296
Structured prediction involves learning to predict complex structures rather than simple scalar values. The main challenge arises from the non-Euclidean nature of the output space, which generally requires relaxing the problem formulation. Surrogate
Externí odkaz:
http://arxiv.org/abs/2411.11682
This paper introduces the sigma flow model for the prediction of structured labelings of data observed on Riemannian manifolds, including Euclidean image domains as special case. The approach combines the Laplace-Beltrami framework for image denoisin
Externí odkaz:
http://arxiv.org/abs/2408.15946
We study a theoretical and algorithmic framework for structured prediction in the online learning setting. The problem of structured prediction, i.e. estimating function where the output space lacks a vectorial structure, is well studied in the liter
Externí odkaz:
http://arxiv.org/abs/2406.12366
By leveraging the kernel trick in the output space, kernel-induced losses provide a principled way to define structured output prediction tasks for a wide variety of output modalities. In particular, they have been successfully used in the context of
Externí odkaz:
http://arxiv.org/abs/2406.09253
Autor:
Ahmed, Kareem, Teso, Stefano, Morettin, Paolo, Di Liello, Luca, Ardino, Pierfrancesco, Gobbi, Jacopo, Liang, Yitao, Wang, Eric, Chang, Kai-Wei, Passerini, Andrea, Broeck, Guy Van den
Structured output prediction problems are ubiquitous in machine learning. The prominent approach leverages neural networks as powerful feature extractors, otherwise assuming the independence of the outputs. These outputs, however, jointly encode an o
Externí odkaz:
http://arxiv.org/abs/2405.07387
Optimization layers in deep neural networks have enjoyed a growing popularity in structured learning, improving the state of the art on a variety of applications. Yet, these pipelines lack interpretability since they are made of two opaque layers: a
Externí odkaz:
http://arxiv.org/abs/2405.18293
This paper studies online structured prediction with full-information feedback. For online multiclass classification, Van der Hoeven (2020) established \emph{finite} surrogate regret bounds, which are independent of the time horizon, by introducing a
Externí odkaz:
http://arxiv.org/abs/2402.08180
Autor:
Shavarani, Hassan S., Sarkar, Anoop
Entity linking is a prominent thread of research focused on structured data creation by linking spans of text to an ontology or knowledge source. We revisit the use of structured prediction for entity linking which classifies each individual input to
Externí odkaz:
http://arxiv.org/abs/2310.14684
We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e.g. event and relation extraction, syntactic and semantic parsing). Our framework requires representing the outputs of these ta
Externí odkaz:
http://arxiv.org/abs/2310.13793