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Found 6 papers, 3 papers with code
Wide & deep learning for spatial & intensity adaptive image restoration
The wide & deep architecture in DparNet enables the learned parameters to directly modulate the final restoring results, boosting spatial & intensity adaptive image restoration.
Community Question Answering Entity Linking via Leveraging Auxiliary Data
Community Question Answering (CQA) platforms contain plenty of CQA texts (i. e., questions and answers corresponding to the question) where named entities appear ubiquitously.
Combinatorial Testing for Deep Learning Systems
Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications.
DeepMutation: Mutation Testing of Deep Learning Systems
To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i. e., training data and training programs).
Software Engineering
DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems
Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data.
Comparison among dimensionality reduction techniques based on Random Projection for cancer classification
FS followed by RP outperforms other combination methods in classification accuracy on most of the datasets.
