Search Results for author:
Found 3 papers, 1 papers with code
SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations
Explaining the decisions of neural models is crucial for ensuring their trustworthiness at deployment time.
Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection
In this paper, we review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud.
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training
In particular, we discuss attacks against ReLU activation functions that make it possible to generate a family of malicious inputs, which are called fooling inputs, to be used at inference time to induce controlled misclassifications.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
