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Found 5 papers, 3 papers with code
A Method to Predict Semantic Relations on Artificial Intelligence Papers
Predicting the emergence of links in large evolving networks is a difficult task with many practical applications.
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision, focusing on two semi-supervised approaches currently in use.
Ranked #1 on
Supervised Image Retrieval
on CIFAR-10
Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations
Handling this noise in a principled way is an important challenge for machine learning, called learning from crowds.
A Binary Variational Autoencoder for Hashing
Searching a large dataset to find elements that are similar to a sample object is a fundamental problem in computer science.
Ranked #1 on
Text Retrieval
on 20 Newsgroups
Efficient Classification of Multi-Labelled Text Streams by Clashing
Our method is composed of an online procedure used to efficiently map text into a low-dimensional feature space and a partition of this space into a set of regions for which the system extracts and keeps statistics used to predict multi-label text annotations.
