82,076 research outputs found
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learning has shown a great potential to address the difficult
situation like this. However, deep learning technologies conventionally use
grid-like structured data, thus application of deep learning technologies to
the classification of human disease subtypes is yet to be explored. Recently,
graph based deep learning techniques have emerged, which becomes an opportunity
to leverage analyses in network biology. In this paper, we proposed a hybrid
model, which integrates two key components 1) graph convolution neural network
(graph CNN) and 2) relation network (RN). We utilize graph CNN as a component
to learn expression patterns of cooperative gene community, and RN as a
component to learn associations between learned patterns. The proposed model is
applied to the PAM50 breast cancer subtype classification task, the standard
breast cancer subtype classification of clinical utility. In experiments of
both subtype classification and patient survival analysis, our proposed method
achieved significantly better performances than existing methods. We believe
that this work is an important starting point to realize the upcoming
personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201
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