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Found 15 papers, 0 papers with code
Erratum: Link prediction in drug-target interactions network using similarity indices
Conclusion: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.
Bimodal network architectures for automatic generation of image annotation from text
The source of big data is typically large image collections and clinical reports recorded for these images.
Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents
Inferring the information structure of scientific documents is useful for many NLP applications.
Native Language Identification Using Large, Longitudinal Data
Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts.
BIG-bench Machine Learning
Native Language Identification
+1
Towards Visual Dialog for Radiology
Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-the-art models.
Automatic semantic classification of scientific literature according to the hallmarks of cancer
The hallmarks of cancer have become highly influential in cancer research.
When and Why does a Model Fail? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis
Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.
