A key bottleneck in structured output prediction is the need for inference during training and testing, usually requiring some form of dynamic programming.
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To do so, we formulate this problem as an embedding inference problem where the constraints are imposed onto the embeddings of labels by geometric construction.
We have demonstrated a novel approach to structured multilabel prediction where inference is re- placed with constraints on the score model. On multilabel ...
Structured multi-label prediction is a task aiming to associate every object with multiple labels that are semantically constrained in a structured manner (e.g ...
Oct 31, 2022 · This paper introduces a method for multi-label classification when the class labels have known dependency structures, namely implication and ...
This work proposes to embed prediction constraints directly into the learned representation by eliminating the need for explicit inference, so that a more ...
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Structured multi-label prediction is a task aiming to associate every object with multiple labels that are semantically constrained in a structured manner (e.g ...
Apr 3, 2024 · We consider a structured multi-label prediction problem where the labels are organized under implication and mutual exclusion constraints.
Nov 1, 2022 · Xiong, Bo ; Cochez, Michael ; Nayyeri, Mojtaba et al. / Hyperbolic Embedding Inference for Structured Multi-Label Prediction. NeurIPS2022. 2022.