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Found 4 papers, 3 papers with code
Learning to Weight for Text Classification
In information retrieval (IR) and related tasks, term weighting approaches typically consider the frequency of the term in the document and in the collection in order to compute a score reflecting the importance of the term for the document.
A Recurrent Neural Network for Sentiment Quantification
Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have been attributed to each class.
Exploring epoch-dependent stochastic residual networks
The recently proposed stochastic residual networks selectively activate or bypass the layers during training, based on independent stochastic choices, each of which following a probability distribution that is fixed in advance.
Picture It In Your Mind: Generating High Level Visual Representations From Textual Descriptions
We choose to implement the actual search process as a similarity search in a visual feature space, by learning to translate a textual query into a visual representation.
