177 research outputs found
Concept Discovery for Fast Adapatation
The advances in deep learning have enabled machine learning methods to
outperform human beings in various areas, but it remains a great challenge for
a well-trained model to quickly adapt to a new task. One promising solution to
realize this goal is through meta-learning, also known as learning to learn,
which has achieved promising results in few-shot learning. However, current
approaches are still enormously different from human beings' learning process,
especially in the ability to extract structural and transferable knowledge.
This drawback makes current meta-learning frameworks non-interpretable and hard
to extend to more complex tasks. We tackle this problem by introducing concept
discovery to the few-shot learning problem, where we achieve more effective
adaptation by meta-learning the structure among the data features, leading to a
composite representation of the data. Our proposed method Concept-Based
Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent
improvements in the structured data for both synthesized datasets and
real-world datasets.Comment: SDM2
Flow-based Influence Graph Visual Summarization
Visually mining a large influence graph is appealing yet challenging. People
are amazed by pictures of newscasting graph on Twitter, engaged by hidden
citation networks in academics, nevertheless often troubled by the unpleasant
readability of the underlying visualization. Existing summarization methods
enhance the graph visualization with blocked views, but have adverse effect on
the latent influence structure. How can we visually summarize a large graph to
maximize influence flows? In particular, how can we illustrate the impact of an
individual node through the summarization? Can we maintain the appealing graph
metaphor while preserving both the overall influence pattern and fine
readability?
To answer these questions, we first formally define the influence graph
summarization problem. Second, we propose an end-to-end framework to solve the
new problem. Our method can not only highlight the flow-based influence
patterns in the visual summarization, but also inherently support rich graph
attributes. Last, we present a theoretic analysis and report our experiment
results. Both evidences demonstrate that our framework can effectively
approximate the proposed influence graph summarization objective while
outperforming previous methods in a typical scenario of visually mining
academic citation networks.Comment: to appear in IEEE International Conference on Data Mining (ICDM),
Shen Zhen, China, December 201
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