Search Results for author:
Found 10 papers, 1 papers with code
Federated Unlearning for Human Activity Recognition
In response, we propose a lightweight machine unlearning method for refining the FL HAR model by selectively removing a portion of a client's training data.
A First Look at Information Highlighting in Stack Overflow Answers
For training recommendation models, we choose CNN and BERT models for each type of formatting (i. e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers.
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation
In this paper, we propose a uniform network to fill both the gaps, termed FGNet.
The Impact of Using Regression Models to Build Defect Classifiers
We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches; ii) In contrast to common practice, building a defect classifier using discretized defect counts (i. e., discretized classifiers) does not always lead to better performance.
Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e. g., median).
The impact of feature importance methods on the interpretation of defect classifiers
We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods).
Hippocampus-heuristic Character Recognition Network for Zero-shot Learning
To this end, this paper proposes a novel Hippocampus-heuristic Character Recognition Network (HCRN), which references the way of hippocampus thinking, and can recognize unseen Chinese characters (namely zero-shot learning) only by training part of radicals.
RL-CSDia: Representation Learning of Computer Science Diagrams
Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank.
Aggregating Votes with Local Differential Privacy: Usefulness, Soundness vs. Indistinguishability
In this work, after theoretically quantifying the estimation error bound and the manipulating risk bound of the Laplace mechanism, we propose two mechanisms improving the usefulness and soundness simultaneously: the weighted sampling mechanism and the additive mechanism.
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning.
