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Found 6 papers, 1 papers with code
MTLB-STRUCT @PARSEME 2020: Capturing Unseen Multiword Expressions Using Multi-task Learning and Pre-trained Masked Language Models
This paper describes a semi-supervised system that jointly learns verbal multiword expressions (VMWEs) and dependency parse trees as an auxiliary task.
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks.
SEVEN: Deep Semi-supervised Verification Networks
The model consists of two complementary components.
Deep Multi-view Models for Glitch Classification
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO.
Semi-supervised Deep Representation Learning for Multi-View Problems
While neural networks for learning representation of multi-view data have been previously proposed as one of the state-of-the-art multi-view dimension reduction techniques, how to make the representation discriminative with only a small amount of labeled data is not well-studied.
Dimensionality Reduction
Learning Representation Of Multi-View Data
Leveraging Semi-Supervised Learning for Fairness using Neural Networks
There has been a growing concern about the fairness of decision-making systems based on machine learning.
