7,110 research outputs found
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
During time-critical situations such as natural disasters, rapid
classification of data posted on social networks by affected people is useful
for humanitarian organizations to gain situational awareness and to plan
response efforts. However, the scarcity of labeled data in the early hours of a
crisis hinders machine learning tasks thus delays crisis response. In this
work, we propose to use an inductive semi-supervised technique to utilize
unlabeled data, which is often abundant at the onset of a crisis event, along
with fewer labeled data. Specif- ically, we adopt a graph-based deep learning
framework to learn an inductive semi-supervised model. We use two real-world
crisis datasets from Twitter to evaluate the proposed approach. Our results
show significant improvements using unlabeled data as compared to only using
labeled data.Comment: 5 pages. arXiv admin note: substantial text overlap with
arXiv:1805.0515
Bayesian Semi-supervised Learning with Graph Gaussian Processes
We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.Comment: To appear in NIPS 2018 Fixed an error in Figure 2. The previous arxiv
version contains two identical sub-figure
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