Text Emotion Distribution Learning via Multi-Task
sentation label, while such ambiguity characteristic is ignored in the SLL and MLL methods Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) 4595
Exploit Bounding Box Annotations for Multi-Label Object
sentation (feature view) for multi-label object recognition Another novelty of our work is the proposed LMNN CNN which effectively extracts local information from the strong labels 2 Related Works Our paper mainly relates to the topics of CNN based multi-label object recognition, multi-view and multi-instance learning and local and metric
Hierarchy-Aware Global Model for Hierarchical Text Classification
extracts label-wise text features with hierarchy en-coders based on prior hierarchy information Moreover, the attention mechanism is introduced in MLC byMullenbach et al (2018) for ICD cod-ing Rios and Kavuluru(2018) trains label repre-sentation through basic GraphCNN and conducts mutli-label attention with residual shortcuts At-
Labeled LDA: A supervised topic model for credit attribution
label set Consequently, these models fall short as a solution to the credit attribution problem Be-cause labels have meaning to the people that as-signed them, a simple solution to the credit attri-bution problem is to assign a document’s words to its labels rather than to a latent and possibly less interpretable semantic space
Nonparametric Guidance of Autoencoder Representations using
the autoencoder training objective by adding label-specific output units in addition to the recon-struction This approach was also followed by Ranzato and Szummer (2008) for learning document representations The difficulty of this approach is that it complicates the task of learning the autoencoder repre-sentation
A Label Independent Memory for Semi-Supervised Few-shot Video
to assign labels to the unlabeled data However, instead of assigning the class label to each individual data point, we introduce a label independent memory bank to store the relatedness between the unlabeled data and the target examples The label will be assigned to the class prototype, which is a weighted average of the features in each LIM
Orderless Recurrent Models for Multi-Label Classification
sentation to feed to the RNN: in [48], images and labels are projected to the same low-dimensional space to model the image-text relationship, while [32] uses the predicted class
Self-supervised Label Augmentation via Input Transformations
2009) (10 labels) with the self-supervision on rotation (4 labels), we learn the joint probability distribution on all possible combinations, i e , 40 labels This label augmentation method, which we refer to as self-supervised label augmentation (SLA), does not force any invariance to the transformations without assumption for the
Simple, Fast, Accurate Intent Classification and Slot
label-recurrent, and non-recurrent Recent state-of-the-art models fall into the first category, as encoder-decoder architectures have recurrent en-coders to perform word context encoding, and pre-dict slot label sequences using recurrent decoders that use both word and label information as they decode (Hakkani-Tur et al ¨ ,2016;Liu and Lane,
Attention as Relation: Learning Supervised Multi-head Self
tion into a multi-label classification task and design a supervised multi-head self-attention mechanism • Extensive experiments are conducted on two benchmark datasets, and the results show that our model achieves state-of-the-art performances with 1 3 and 14 2 im-provements, respectively 2 Related Work
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