Search Results for author:
Found 11 papers, 4 papers with code
Safe-Embed: Unveiling the Safety-Critical Knowledge of Sentence Encoders
This paper investigates the potential of sentence encoders to distinguish safe from unsafe prompts, and the ability to classify various unsafe prompts according to a safety taxonomy.
Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions
The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations.
A fast and integrative algorithm for clustering performance evaluation in author name disambiguation
Details of the integrative calculation are described with examples and pseudo-code to assist scholars to implement each measure easily and validate the correctness of implementation.
ORCID-linked labeled data for evaluating author name disambiguation at scale
This study suggests that the open researcher profile system, ORCID, can be used as an authority source to label name instances at scale.
Effect of forename string on author name disambiguation
In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author.
Generating automatically labeled data for author name disambiguation: An iterative clustering method
Several challenges are discussed for applying this method to resolving author name ambiguity in large-scale scholarly data.
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks
For the gradient computation across the time domain in Spiking Neural Networks (SNNs) training, two different approaches have been independently studied.
BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
Binary Neural Networks (BNNs) have been garnering interest thanks to their compute cost reduction and memory savings.
The impact of imbalanced training data on machine learning for author name disambiguation
Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it.
Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics
Artificial Neural Network computation relies on intensive vector-matrix multiplications.
