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Found 5 papers, 1 papers with code
Learning with Partial Labels from Semi-supervised Perspective
Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP).
Who Is Your Right Mixup Partner in Positive and Unlabeled Learning
In this paper, we propose a novel PU learning method, namely Positive and unlabeled learning with Partially Positive Mixup (P3Mix), which simultaneously benefits from data augmentation and supervision correction with a heuristic mixup technique.
Semi-Supervised Text Classification with Balanced Deep Representation Distributions
They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts.
Topic representation: finding more representative words in topic models
The top word list, i. e., the top-M words with highest marginal probability in a given topic, is the standard topic representation in topic models.
Integrating Topic Modeling with Word Embeddings by Mixtures of vMFs
Gaussian LDA integrates topic modeling with word embeddings by replacing discrete topic distribution over word types with multivariate Gaussian distribution on the embedding space.
