Search Results for author:
Found 9 papers, 2 papers with code
The Topic Confusion Task: A Novel Evaluation Scenario for Authorship Attribution
Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.
Pluvio: Assembly Clone Search for Out-of-domain Architectures and Libraries through Transfer Learning and Conditional Variational Information Bottleneck
The practice of code reuse is crucial in software development for a faster and more efficient development lifecycle.
On the Effectiveness of Interpretable Feedforward Neural Network
We conclude by finding that the generalized IFFNNs achieve comparable classification performance to their normal feedforward neural network counterparts and provide meaningful interpretations.
The Topic Confusion Task: A Novel Scenario for Authorship Attribution
Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors.
Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e. g., correspondence between medications and diagnoses) can often be missing in practice.
Deep Learning-based Stress Determinator for Mouse Psychiatric Analysis using Hippocampus Activity
Decoding neurons to extract information from transmission and employ them into other use is the goal of neuroscientists' study.
I-MAD: Interpretable Malware Detector Using Galaxy Transformer
It also incorporates our proposed interpretable feed-forward neural network to provide interpretations for its detection results by quantifying the impact of each feature with respect to the prediction.
ER-AE: Differentially Private Text Generation for Authorship Anonymization
By augmenting the semantic information through a REINFORCE training reward function, the model can generate differentially private text that has a close semantic and similar grammatical structure to the original text while removing personal traits of the writing style.
Learning Stylometric Representations for Authorship Analysis
Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data.
